# Resource Management and Secure Data Exchange for Mobile Sensors Using Ethereum Blockchain
## 5 Results
### Accomplished Result
* _Novelty:_ A closer look at the overhead outcome shows that it is much better, even in contrast to the other previously assessed performance metrics in the proposed analysis. This is because of the novel features introduced in the proposed system associated with the redistribution and reassignment of resources. When any mobile sensor is assigned a surplus resource, the surplus resources are autonomously assigned to other needy mobile users, which is not observed in the existing approaches [38; 39; 40; 41; 42; 43; 44; 45; 46; 47]. This phenomenon leads to a fair redistribution of resources, reducing the possibilities of fluctuating network size, network density, and a higher degree of topology changes. * _Evidential Justification:_ In densely populated simulation areas, the control overhead increases because of the significant number of neighboring nodes and the frequency of route discovery and maintenance messages. Sparse networks may have lower control overhead but may suffer from longer route establishment times. This problem is addressed in the proposed system by adopting a network model based on a novel cost modeling that controls the network's density. This characteristic is not observed in existing approaches [38; 39; 40; 41; 42; 43; 44; 45; 46; 47], which have the least control over overhead increments. In addition, dynamic network conditions, such as node mobility or failures, can trigger frequent topology changes, leading to an increased control overhead. The proposed system is a network model that can adapt to these changes by updating the data dissemination information and recalculating paths, thereby contributing to reduced control message transmission. Furthermore, in the existing system, the transmission range of the sensor nodes affects the frequency of neighbor discovery and synchronization messages. Nodes within range of each other must exchange control messages to establish and maintain links, leading to a higher control overhead in networks with more extensive transmission ranges. It is also deemed that the existing approaches [38; 39; 40; 41; 42; 43; 44; 45; 46; 47] use stringent QoS requirements and may require more frequent control message exchanges to maintain the desired performance levels, leading to a higher control overhead. * _Quantified Outcome_: The proposed system contributes to an overhead reduction of approximately 38% compared with existing approaches. * **Cost**: This is another significant performance metric for establishing a cost-effective model. The computation of this metric is carried out by considering all possible networks and computation-based resources used to transmit data successfully. The network resources include channel capacity, whereas computational resources include the assigned energy/power resources. The learning outcomes of this performance metric, as shown in Figure 10, are as follows:
* _Novelty:_ The core novelty of the proposed outcome is that it computes the cost following the user's payment of the cost of using the services, unlike any reported schemes in the literature (e.g., [PERSON] et al. [39], [PERSON] et al. [48], [PERSON] et al. [29]). The novelty of this scheme is that when the saturation rate of the server resources is higher, the resource unit price will also be higher. This optimal cost prompts the user to select resources using N2N or N2S modes of communication. Hence, if the utilization of resources is higher, it can contribute towards minimizing the possibilities of bottleneck conditions in the network. Therefore, the proposed system contributes to both QoS and experience. _Evidential Justification_: A closer look at existing approaches shows various dedicated efforts to comply with cost-effective outcomes. The cost-effectiveness of [PERSON] et al. [39] is associated with the optimal execution time of the blocks and multiparty signatures. [PERSON] et al. [48] used machine learning to minimize costs, whereas [PERSON] et al. [29] used blockchain to minimize operational costs.