Mobile data collectors that can be used without a central control mechanism currently have common use in many fields. Because they do not need a central unit, each node in a network can move independently. The field literature offers various group- or entity-based models to define the functioning of mobile data collectors. In this study, a random entity mobility model (REMM) research was performed. The study was based on the models random walk (RW) and random waypoint (RWP), used in several former studies mentioned in the literature. Furthermore, the models random point (RP) and random journey (RJ) proposed by Bilgin [1] for two-dimensional (2D) space were transferred to three-dimensional (3D–cubic) to be used in the study. Study findings obtained by defining a various number of fixed nodes in areas of various sizes were analyzed using 4 different metrics. It was observed that 4 different metric values decreased for 4 REMMs when the cubic area was enlarged by increasing the edge lengths (150–200–250 pixel) of the cubic. When the cubic’s edge length is 150-200-250 pixel, respectively, connected node ratio (CNR) metric value is 98.04%–95.8%–91.34% for RP and 96.83%-83.23%-70% for RJ. Provided that the cubic area remains constant, the increases in the number of nodes generally tend to increase, although there are slight fluctuations on the results. When the cubic edge is 200 and the node numbers are 4–6–8–10, the message delay is 13.345–16.566–27.386–40.050 seconds for RW and 6.579–9.124–11.431–13.456 seconds for RWP. In the comparisons made by taking the average of the values obtained according to the size of the cubic area and the number of nodes, the RP model reached the highest values for all metrics. For example, the visited node ratio (VNR) metric average for the cubic edge 200 pixels is 98.76% for RP and 94.68%–87.38%–94.78% for RW–RWP–RJ. The VNR metric for the cubic edge 250 is 96.55%–93.7%–87.45%–51.27% for the RP–RW–RWP–RJ. Similarly, the average values obtained for other metrics prove this situation. In addition, when the results of the study are examined, it has been measured that the RP model can deliver the message to the base with less delay than other models. The average delay for the cubic edge 150 is 2.933–27.667–23.236–5.698 second for the RP–RW–RWP–RJ and 2.846–24.337–10.148-4.293 second when the edge is 200. When the average results obtained were examined, the success ranking in the delay metric was RP–RJ–RWP and RW, while the other metrics were formed as RP–RJ–RW–RWP. Considering all the obtained results, it was seen that the proposed two models achieved better results than the existing models in 3D after 2D.

可以在没有中央控制机制的情况下使用的移动数据收集器目前在许多领域都有共同的用途。因为它们不需要中央单元，所以网络中的每个节点都可以独立移动。现场文献提供了各种基于组或实体的模型来定义移动数据收集器的功能。在本研究中，进行了随机实体移动模型 (REMM) 研究。该研究基于模型随机游走 (RW) 和随机航路点 (RWP)，在文献中提到的几项以前的研究中使用过。此外，Bilgin [1] 为二维（2D）空间提出的模型随机点（RP）和随机旅程（RJ）被转移到三维（3D-cubic）以用于研究。通过在不同大小的区域中定义不同数量的固定节点而获得的研究结果使用 4 种不同的指标进行分析。据观察，当通过增加立方体的边缘长度（150-200-250 像素）来扩大立方体面积时，4 个 REMM 的 4 个不同的度量值会降低。当立方体的边长分别为 150-200-250 像素时，连接节点比率 (CNR) 度量值为 RP 的 98.04%–95.8%–91.34% 和 RJ 的 96.83%-83.23%-70%。如果立方面积保持不变，节点数量的增加通常会增加，尽管结果会有轻微的波动。当三次边为 200 且节点数为 4-6-8-10 时，RW 的消息延迟为 13.345-16.566-27.386-40.050 秒，RWP 的消息延迟为 6.579-9.124-11.431-13.456 秒。在根据立方体面积的大小和节点数取值的平均值进行比较时，RP 模型在所有指标上都达到了最高值。例如，立方边 200 个像素的访问节点比率 (VNR) 度量平均值对于 RP 为 98.76%，对于 RW-RWP-RJ 为 94.68%–87.38%–94.78%。对于 RP–RW–RWP–RJ，三次边 250 的 VNR 度量为 96.55%–93.7%–87.45%–51.27%。类似地，为其他指标获得的平均值证明了这种情况。此外，在检查研究结果时，已经测量到 RP 模型可以以比其他模型更少的延迟将消息传递到基地。对于 RP-RW-RWP-RJ，三次边沿 150 的平均延迟为 2.933-27.667-23.236-5.698 秒，边沿为 200 时为 2.846-24.337-10.148-4.293 秒。当检查获得的平均结果时，延迟度量中的成功排名是 RP-RJ-RWP 和 RW，而其他度量则形成为 RP-RJ-RW-RWP。考虑到所有获得的结果，可以看出所提出的两个模型在 2D 之后的 3D 中取得了比现有模型更好的结果。