An emerging class of interactive wearable cognitive assistance  applications is poised to become one of the key demonstrators of edge  computing infrastructure. In this paper, we design seven such  applications and evaluate their performance in terms of latency across a  range of edge computing configurations, mobile hardware, and wireless  networks, including 4G LTE. We also devise a novel multi-algorithm  approach that leverages temporal locality to reduce end-to-end latency  by 60% to 70%, without sacrificing accuracy. Finally, we derive target  latencies for our applications, and show that edge computing is crucial  to meeting these targets.

Chen, Z., Hu, W., Wang, J., Zhao, S., Amos, B., Wu, G., Ha, K., Elgazzar, K., Pillai, P., Klatzky, R., Siewiorek, D.,  Satyanarayanan, M.
Proceedings of the Second ACM/IEEE Symposium on Edge Computing, Fremont, CA, October 2017