Designing Mobile Collaborative Applications for Cloud Environments

Author(s):  
Nadir Guetmi ◽  
Abdessamad Imine

Mobile devices have experienced a huge progress in the capacity of computing, storage and data visualization. They are becoming the device of choice for operating a large variety of applications while supporting real-time collaboration of people and their mobility. Despite this progress, the energy consumption and the network coverage remain a serious problem against an efficient and continuous use of these mobile collaborative applications and a great challenge for their designers and developers. To address these issues, this chapter describes design patterns that help modelling mobile collaborative applications to support collaboration through the cloud. Two levels are presented: the first level provides self-control to create clones of mobile devices, manage users' groups and recover failed clones in the cloud. The second level supports group collaboration mechanisms in real-time. These design patterns have been used as a basis for the design of a mobile collaborative editing application.

Author(s):  
Erica Fong ◽  
Dickson K.W. Chiu ◽  
Haiyang Hu ◽  
Yi Zhuang ◽  
Hua Hu

Peak electricity demands from huge number of households in a mega-city would cause contention, leading to potential blackout. This paper proposes bi-directional collaboration via a Smart Energy Monitor System (SEMS) between consumers and energy suppliers, exchanging real-time energy usage data with smart meters over the Internet and mobile devices for well-informed decisions and even predictions. The authors further propose the use of an Alert Management System (AMS) to monitor and aggregate critical energy consumption events for this purpose.


Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 229
Author(s):  
Xianzhong Tian ◽  
Juan Zhu ◽  
Ting Xu ◽  
Yanjun Li

The latest results in Deep Neural Networks (DNNs) have greatly improved the accuracy and performance of a variety of intelligent applications. However, running such computation-intensive DNN-based applications on resource-constrained mobile devices definitely leads to long latency and huge energy consumption. The traditional way is performing DNNs in the central cloud, but it requires significant amounts of data to be transferred to the cloud over the wireless network and also results in long latency. To solve this problem, offloading partial DNN computation to edge clouds has been proposed, to realize the collaborative execution between mobile devices and edge clouds. In addition, the mobility of mobile devices is easily to cause the computation offloading failure. In this paper, we develop a mobility-included DNN partition offloading algorithm (MDPO) to adapt to user’s mobility. The objective of MDPO is minimizing the total latency of completing a DNN job when the mobile user is moving. The MDPO algorithm is suitable for both DNNs with chain topology and graphic topology. We evaluate the performance of our proposed MDPO compared to local-only execution and edge-only execution, experiments show that MDPO significantly reduces the total latency and improves the performance of DNN, and MDPO can adjust well to different network conditions.


Author(s):  
Gabriel de Souza Pereira Moreira ◽  
Denis Ávila Montini ◽  
Daniela América da Silva ◽  
Felipe Rafael Motta Cardoso ◽  
Luiz Alberto Vieira Dias ◽  
...  

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