Human Mobility Support for Personalised Data Offloading
Lima, E. Lima
; Carvalho, P.M.C.
; Viana, A.V.
IEEE Transactions on Network and Service Management Vol. 19, Nº 2, pp. 1505 - 1520, February, 2022.
ISSN (print): 1932-4537
Scimago Journal Ranking: 1,69 (in 2022)
Digital Object Identifier: 10.1109/TNSM.2022.3153804
Mobile data offloading systems can use WiFi networks to offload data or computation tasks while the users are traveling. Due to the limited coverage of a WiFi AP, the expected offloading performance of such systems is linked with the users' mobility. In this work, human mobility is investigated from an offloading perspective. Offloading Regions (ORs) are extracted from real-world mobility traces and offloading opportunities are analyzed. Results show that ORs can be used to offload while users are in transit. Offloading mobility predictability, although crucial, shows to be rather challenging, expressed by the poor predictive performance of well-known models in the literature (~37% acc. for the best predictor). An extensive analysis shows a predictive performance improvement up to ~35% if mobility regularity properties are considered. The trade-off between mobility prediction and offloading opportunities is also investigated, demonstrating the need for offloading systems to adopt hybrid strategies, i.e., mixing opportunistic and predictive strategies. Moreover, offloading mobility indicates that mobile devices do not need to perform multiple handovers during the offloading process as offloading sites can be covered by a small number of APs. Finally, conclusions and findings regarding the offloading mobility properties studied in this work are shown to be valid for different urban scenarios given the high degree of similarity between the results from two different mobility datasets.