Abstract
Since human movement patterns are important for validating the performance of wireless networks, several traces of human movements in real life have been collected. However, collecting data about human movements is costly and time-consuming. Moreover, multiple traces are demanded to test various network scenarios. As a result, a lot of synthetic models of human movement have been proposed. Nevertheless, most of the proposed models were often based on random generation, and cannot produce realistic human movements. Although there have been a few models that tried to capture the characteristics of human movement in real life (e.g., flights, inter-contact times, and pause times following the truncated power-law distribution), those models still cannot reflect realistic human movements due to a lack of consideration for social context among people. To address those limitations, in this paper, we propose a novel human mobility model called the social relationship–aware human mobility model (SRMM), which considers social context as well as the characteristics of human movement. SRMM partitions people into social groups by exploiting information from a social graph. Then, the movements of people are determined by considering the distances to places and social relationships. The proposed model is first evaluated by using a synthetic map, and then a real road map is considered. The results of SRMM are compared with a real trace and other synthetic mobility models. The obtained results indicate that SRMM is consistently better at reflecting both human movement characteristics and social relationships.
Highlights
Human movement patterns greatly affect the performance of various wireless networks, such as opportunistic mobile social networks and delay-tolerant networks, which rely on human movement for pairwise contacts between two communicating devices
To address the limitations in existing models, we propose a new human mobility model called the social relationship−aware human mobility model (SRMM), which takes into account social relationships among people and human movement characteristics
The results of complexities show that SRMM takes a longer time than others mobility models since similar least-action walk (SLAW) does not consider social relationships in selecting the destination, CMM does not take into account generating spots in the area and considering the distance in selecting the destination spot, and in ORBIT, the most of steps are random processes
Summary
Human movement patterns greatly affect the performance of various wireless networks, such as opportunistic mobile social networks and delay-tolerant networks, which rely on human movement for pairwise contacts between two communicating devices. Several studies have seriously analyzed real human movement traces and found interesting human movement characteristics where flights, inter-contact times (ICTs), and pause times follow truncated power-law distributions [3,4]. Such models did not consider the social context among people They could not fully reflect human movement in real life. The models did not consider human movement characteristics (e.g., flights, ICTs, the radius of gyration, and pause-time distributions). Selecting the destinations of people is only affected by their social ties without considering important contexts (e.g., in real life, the places an individual visit during different day trips are correlated, and people tend to visit nearby places) For those reasons, such models could not reflect realistic human movement. SRMM considers the characteristics of human movements in terms of flights, ICTs, the radius of gyration, and pause-time distributions.
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