BreadCrumbs: forecasting mobile connectivity
Proceedings of the 14th ACM international conference on Mobile computing and networking
Augmenting mobile 3G using WiFi
Proceedings of the 8th international conference on Mobile systems, applications, and services
A first look at traffic on smartphones
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
A first look at mobile hand-held device traffic
PAM'10 Proceedings of the 11th international conference on Passive and active measurement
Mobile data offloading: how much can WiFi deliver?
Proceedings of the 6th International COnference
ICNP '11 Proceedings of the 2011 19th IEEE International Conference on Network Protocols
Mobile Data Offloading through Opportunistic Communications and Social Participation
IEEE Transactions on Mobile Computing
Casting doubts on the viability of WiFi offloading
Proceedings of the 2012 ACM SIGCOMM workshop on Cellular networks: operations, challenges, and future design
Bridging the gap between internet standardization and networking research
ACM SIGCOMM Computer Communication Review
Hi-index | 0.00 |
Searching for mobile data offloading solutions has become topical in recent years. In the Metropolitan Advanced Delivery Network (MADNet) project, we explore the potential of multiple interfaces on smartphones through a collaborative design to enable energy-aware WiFi-based mobile data offloading. We advocate that the vertical collaboration across different wireless access technologies and network vendors is the key to aggregate the power of mobile operators, WiFi service providers, and end-users. Aiming at reducing the energy consumption on smartphones, we propose an energy-aware algorithm supported by our collaborative architecture to avoid offloading to low-throughput WiFi networks which may consume more energy. The evaluation of streaming applications on our prototyped Nokia N900 smartphones demonstrates that we are able to achieve more than 80% energy saving. Our experiment in the wild also shows that our design can tolerate the minor errors of localization, mobility prediction, and offload capacity estimation.