  In the context of changing travel behaviors and the expanding user base of
Geographic Information System (GIS) services, conventional centralized
architectures responsible for handling shortest distance queries are facing
increasing challenges, such as heightened load pressure and longer response
times. To mitigate these concerns, this study is the first to develop an edge
computing framework specially tailored for processing distance queries. In
conjunction with this innovative system, we have developed a straightforward,
yet effective, labeling technique termed Border Labeling. Furthermore, we have
devised and implemented a range of query strategies intended to capitalize on
the capabilities of the edge computing infrastructure. Our experiments
demonstrate that our solution surpasses other methods in terms of both indexing
time and query speed across various road network datasets. The empirical
evidence from our experiments supports the claim that our edge computing
architecture significantly reduces the latency encountered by end-users, thus
markedly decreasing their waiting times.
