Gearing resource-poor mobile devices with powerful clouds: architectures, challenges, and applications

By Fangming Liu, Peng Shu, Hai Jin, Li Ding, Jie Yu, Di Niu, Bo Li. IEEE wireless communications, 2013

366 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/b4d99b0fc1d98abe7e33f7af492c4a8b229dec73

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Jie Xu
2. Saugata Ghose
3. Rachata Ausavarungnirun
4. Shuguang Cui
5. Feng Wang
6. Onur Mutlu
7. Xin Wang
8. Athanasios V. Vasilakos

In Electrical and Electronic Engineering

1. Jie Xu
2. Shuguang Cui
3. Feng Wang
4. Saugata Ghose
5. Rachata Ausavarungnirun
6. Xin Wang
7. Onur Mutlu
8. Chau Yuen

Citations in the corpus

• Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing (2014, background): “This idea was suggested in [17], [27], with the introduction of cloudlets , providing proximity radio access to fixed servers through Wi-Fi.”
• Google Workloads for Consumer Devices: Mitigating Data Movement Bottlenecks (2018, background): “The performance requirements of these consumer devices have increased dramatically every year to support emerging applications such as 4K video streaming and recording [154], virtual reality (VR) [87], and augmented reality (AR) [6, 18, 60, 93].”
• Joint offloading and computing optimization in wireless powered mobile-edge computing systems (2017, background): “beamforming system for multiple energy transmitters to charge multiple energy receivers simultaneously, with the help of the energy measurement and feedback. On the other hand, several existing works [25]–[33] investigated the energy-efficient multiuser MEC design, where each user is powered by fixed ”
• D2D Fogging: An Energy-Efficient and Incentive-Aware Task Offloading Framework via Network-assisted D2D Collaboration (2016, background): “Since the energy issue has been a key concern and the critical bottleneck of mobile devices [2], [3], [25], the main objective is to make the best effort to reduce the total energy consumption for task executions of all users via D2D collaboration.”
• Mobile Cloud Computing: A Survey, State of Art and Future Directions (2013, background): “Given the current commercial hype and the fact that there are diverse ways of cooperating cloud computing and mobile applications, it is not surprising that there is no single widely accepted clear definition of MCC, as a result its scope can not be properly defined [1].”
• Security in cloud computing: Opportunities and challenges (2015, background): “The mobile device can be the source of user location leakage especially due to location based services [63,81].”
• A Distributed Truthful Auction Mechanism for Task Allocation in Mobile Cloud Computing (2018, background): “nearby mobile devices through WLAN/WiFi can significantly reduce the communication latency and network congestion [13], [14], [15], [16], [17], [18], [19].”
• AVE: Autonomous Vehicular Edge Computing Framework with ACO-Based Scheduling (2017, background): “This concept was quickly transplanted into the mobile environment, where it is also known as mobile cloud computing (MCC) [15].”
• Multiobjective Optimization for Computation Offloading in Fog Computing (2018, background): “However, due to the restrictions of the MDs on size, weight, battery life, and heat dissipation, the gap between the capability of limited computing resources and demand for executing complex applications is gradually increasing [1].”