A Survey of Mobile Cloud Computing

Mobile Cloud Computing （MCC） is emerging as one of the most important branches of cloud computing. In this paper, MCC is defined as cloud computing extended by mobility, and a new ad-hoc infrastructure based on mobile devices. It provides mobile users with data storage and processing services on a cloud computing platform. Because mobile cloud computing is still in its infancy we aim to clarify confusion that has arisen from different views. Existing works are reviewed, and an overview of recent advances in mobile cloud computing is provided. We investigate representative infrastructures of mobile cloud computing and analyze key components. Moreover, emerging MCC models and services are discussed, and challenging issues are identified that will need to be addressed in future work.

Computation Partitioning in Mobile Cloud Computing: A Survey

Mobile devices are increasingly interacting with clouds,and mobile cloud computing has emerged as a new paradigm.An central topic in mobile cloud computing is computation partitioning,which involves partitioning the execution of applications between the mobile side and cloud side so that execution cost is minimized.This paper discusses computation partitioning in mobile cloud computing.We first present the background and system models of mobile cloud computation partitioning systems.We then describe and compare state-of-the-art mobile computation partitioning in terms of application modeling,profiling,optimization,and implementation.We point out the main research issues and directions and summarize our own works.

Networking-GPS: Cooperative Vehicle Localization Using Commodity GPS in Urban Area

A challenging issue in intelligent transportation systems （ITS） is to accurately locate moving vehicles in urban area. Considerable ef- forts have been made to improve the localization accuracy of standalone GPS receivers. However, through empirical study, we found that the latitude and longitude values generated by GPS receivers fluctuate significantly because of the muhipath effect in urban ar- eas. The relative distances between neighboring vehicles with similar GPS signal data in terms of satellite sets and signal strength are much more stable in such a scenario. In this paper, we propose a cooperative localization algorithm, Networking-GPS, to improve the accuracy of location information for vehicular networks in urban area using commodity GPS receivers. First, atom redundantly rigid graphs of vehicles are constructed according to the similarity of neighboring GPS data. Then, through rigidity expansion, local accura- cy can enforce global accuracy. Extensive simulations based on the real road network and trace data of vehicle mobility demonstrate that Networking-GPS can improve the accuracy of the entire system.

Integrated computer vision algorithms and drone scheduling

1.Introduction Computer vision algorithms have attained significant accuracy in the past decade,among which arguably the most important one is deep neural networks.Unmanned aerial vehicles,commonly called drones,equipped with cameras,offer a convenient,efficient,and cost-effective way of collecting a large set of images.Combining drones and computer vision algorithms can automate the monitoring and surveying of infrastructure systems,for example,car detection(Maria et al.,2016),pedestrian and bicycle volume data collection(Kim,2020),and road degradation survey(Leonardi et al.,2018).However,the existing research has been largely driven by two independent streams of expertise:computer vision and drone scheduling.Computer scientists strive to design more accurate computer vision algorithms without much consideration of how the images are collected,whereas operations researchers endeavor to design drone routing algorithms to collect a given set of images in the most efficient manner.We suggest that the planning of images to collect(number and locations of images,amongst others)and the design of—more often than not,the choice of—computer vision algorithms should be determined holistically instead of independently.Section 2 presents an example to show the number of images to collect depends on the accuracy of the computer vision algorithms.Section 3 lays out the roadmap for future research direction.