Novel random models of entity mobility models and performance analysis of random entity mobility models

Abstract

It has become possible to collect data from geographically large areas with smart devices that are prevalentlyused today. Sensors that are integrated into smart devices make it possible for these devices to receive and transmit datawirelessly. The most important problem of this model that is known as mobile crowd sensing and that allows inferenceson the data obtained from its users is lack of data. The main reason for this problem is the lack of sufficient usage of thesensors on devices by the user. To increase the amount of data collected, while users may be incentivized in various ways,the amount and accuracy of the collected data may be increased by developing random entity mobility models (REMMs).In this study, two new models (random point and random journey) were proposed as alternatives to existing REMMs. Inthe experiment environment that was created to measure the performances of the proposed models, their performanceswere compared to those that are currently used prevalently (random waypoint (RWP), random walk (RW), and randomdirection (RD)). In the experiment environment, the performances were compared in terms of three different metrics(visiting rates of nodes, rates of reaching the basis, and the number of messages they carried to the basis). The greatestincrease in differently sized areas and at different numbers of nodes in the RP model in terms of rates of reaching thebasis was 2.6% compared to RWP, 7% compared to RW, and 46.34% compared to RD, while these values for the numberof nodes that were visited were 3% compared to RWP, 1.5% compared to RW, and 17.67% compared to RD. In the sameconditions in terms of the metric on the number of messages, the model collected 1465.4, 2933.46, and 7260.12 moremessages than those in respectively RWP, RW, and RD. The greatest increase in differently sized areas and at differentnumbers of nodes in the RJ model in terms of reaching the basis was 1% compared to RWP, 3.5% compared to RW, and25% compared to RD, while these values for the number of nodes that were visited were 0.75% compared to RWP, 2%compared to RW, and 21.4% compared to RD. In the same conditions in terms of the metric on the number of messages,the model collected 1109.56, 1534.26, and 4488.5 more messages than those in RWP, RW, and RD, respectively.