Cloud-based data synchronization services such as Dropbox, OneDrive, and Google Drive have attracted a huge number of subscribers and it leads to a tremendous of shared data. How can we utilize the redundancy of these shared data? We have studied it and proposed that a novel network data encoding technique which dynamically eliminates redundancy block of inter-files by previously synchronized or shared (i.e., transmitted) data.
IEEE ICNP (International Conference on Network Protocols, acceptance rate: 18.66%) is one of the premier conferences in the computer networking field,
which is run by rigorous papers of 10 pages.
[ ABSTRACT ]
In this work, we raise a question on why the abundant information previously shared between a server and its client is not effectively utilized in the exchange of a new data which may be highly correlated with the shared data. We formulate this question as an encoding problem that is applicable to general data synchronization services including a wide range of Internet services such as cloud data synchronization, web browsing, messaging, and even data streaming. To this problem, we propose a new encoding technique, SyncCoding that maximally replaces subsets of the data to be transmitted with the coordinates pointing to the matching subsets included in the set of relevant shared data, called references. SyncCoding can be easily integrated into a transport layer protocol such as HTTP and enables significant reduction of network traffic. Our experimental evaluations of SyncCoding implemented in Linux shows that it outperforms existing popular encoding techniques, Brotli, LZMA, Deflate, and Deduplication in two practical use networking applications: cloud data sharing and web browsing. The gains of SyncCoding over Brotli, LZMA, Deflate, and Deduplication in the encoded size to be transmitted are shown to be about 12.4%, 20.1%, 29.9%, and 61.2% in the cloud data sharing and about 78.3%, 79.6%, 86.1%, and 92.9% in the web browsing, respectively. The gains of SyncCoding over Brotli, LZMA, and Deflate when Deduplication is applied in advance are about 7.4%, 10.6%, and 17.4% in the cloud data sharing and about 79.4%, 82.0%, and 83.2% in the web browsing, respectively.
[ Overview of the system design and the evaluation scenarios of two use case: 1) Cloud data sharing (left) and 2) Web browsing (right) ]
So far, there have been no previous works with a satisfying performance in terms of a latency, a recognition accuracy and an energy efficiency at the same time. VehicleSense achieves all of these performances simultaneously by utilizing the unique characteristics of a sound of vehicles and implementing accelerometer-based trigger systems.
IEEE WoWMoM (World of Wireless Mobile and Multimedia Networks) is a conference in the areas of wireless, mobile, and multimedia networking as well as ubiquitous and pervasive systems,
which is run by rigorous papers of 9 pages.
[ ABSTRACT ]
A new transportation mode recognition system for smartphones, VehicleSense that is widely applicable to mobile context-aware services is proposed. VehicleSense aims at achieving three performance objectives: high accuracy, low latency, and low power consumption at once by exploiting sound characteristics captured from the built-in microphone while being on candidate transportations. To attain high energy efficiency, VehicleSense adopts hierarchical accelerometer-based triggers that minimize the activation of the microphone of smartphones. Further, to attain high accuracy and low latency, VehicleSense makes use of non-linear filters that can best extract the transportation sound samples.
Our 186-hour log of sound and accelerometer data collected by seven different Android smartphone models confirms that VehicleSense achieves the recognition accuracy of 98.2% with only 0.5 seconds of sound sampling at the power consumption of 26.1 mW on average for all day monitoring.
[ Spectrogram of Bus Sound, Taxi Sound, and Subway Sound (from left to right)
[ VehicleSense system overview
What if a mobile device can put downloading of volume data such as video, apps, and system updates in the background and continue downloading them whenever WiFi connection becomes available? If this can be unawarely while a user is moving, it will be possible for users to save a lot of cellular data cost. How much can be saved through what process is first in-depth studided!
ACM MobiHoc (Acceptance ratio < 12%) is one of flagship ACM conferences on networking, which is run by rigorous papers of 10 pages.
[ ABSTRACT ]
Smart mobile devices are generating a tremendous amount of data traffic that is putting stress on even the most advanced cellular networks. Delayed offloading has recently been proposed as an efficient mechanism to substantially alleviate this stress. The idea is simple. It allows a mobile device to delay transmission of data packets for a certain amount of time, while it searches WiFi (or similarly femtocell) networks to offload the data during the time. When the time expires, it completes the remaining portion of the delayed transmission through the cellular network that is available at the moment. In this paper, we develop an analytical framework using an embedded Markov process for the delayed offloading system, which provides a closed-form expression for estimating how much data generated by the users can be offloaded to WiFi networks from cellular networks even when there are non- Markovian data arrivals and service interruptions. We provide extensive numerical studies with various ranges of delay, service interruption time, arrived data, and service rate. These numerical studies show that the current deployment of WiFi networks measured from a metropolitan city is capable of offloading about 80% of the generated data with 30 minutes of delay and 1 Mbps of WiFi data rate, but increasing the data rate does not help improve the amount of offloading. Further studies using this framework on two new deployment strategies of WiFi networks in the near future give guidance on how to upgrade WiFi networks by revealing that the amount of offloading for 30 minutes of delay and 1 Mbps of data rate can be drastically improved to about 90% or 98% according to the strategy.
CarPool is efficient in a metropolitan city. In a similar manner, can PhonePool will improve the networking performance of nearby mobile devices? We focused on the fact that devices in proximity are sometime subscribing diverse cellular carriers and this can extremely enhance the networking experience, especially when being located in a high speed train (e.g., KTX, TGV, Shinkansen) or in an express bus.
IEEE SECON (Conference on Sensing, Communication, and Networking, Acceptance ratio < 29%) is one of IEEE ComSoc’s quality conferences, which is run by rigorous papers of 9 pages.
[ ABSTRACT ]
Energy consumption for cellular communication is increasingly gaining importance in smartphone battery lifetime as the bandwidth of wireless communication and the demand for mobile traffic increase. For energy-efficient cellular communica- tion, we tackle two energy characteristics of cellular networks: (1) transmission energy highly varies upon channel condition, and (2) transmission of a packet accompanies unnecessary tail energy waste. Under the objective of transmitting packets when the best channel is provided as well as a number of packets are accumulated, we propose a new mobile collaboration framework “PhonePool
” that aggregates smart devices across multiple cellular providers. Compared to the standalone operation, even without a buffering delay, PhonePool
allows better channel and reduces more tail energy in a statistical point of view. To maximize the energy benefit while maintaining the fairness among the nodes in collaboration, we further develop a dynamic programming framework providing the optimal algorithm of PhonePool and its approximated heuristic. Trace-driven simulations on our experi- mental HSPA/EVDO/LTE network traces show that PhonePool of 5 devices achieves up to 42% of energy reduction.