Multi-flow rate control in delayed Wi-Fi offloading systems

Kim, Yeong-Jin; Lee, Joohyun; Jeong, Jaeseong; Chong, Song

doi:10.1109/icoin.2016.7427076

Cited by 7 publications

(6 citation statements)

References 13 publications

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“…Second, a sequential scheduling of a single flow in an EDF manner is motivated by the following rationales that (i) an urgent flow has less opportunities of data transfer than others, and (ii) serving multiple flows concurrently generates frequent switching between flows with active transmission, causing overheads such as switching delay and small data rates due to the need of TCP's ramping-up time (i.e., slow-start). We note that the sequential flow scheduling does not hurt the performance of offloading efficiency when the scheduling interval is short enough and the achievable sum throughput is independent of the number of activated flows; D 2 Sched is an optimal scheduling policy in terms of offloading ratio for a fixed Wi-Fi and cellular throughputs [36]. In spite of time-varying nature of wireless connections, D 2 Sched is expected to work in an highly efficient way, because Cedos's efficient Wi-Fi offloading targets at long flows, and mobile users are typically in indoor Wi-Fi networks, where data rate variations are reasonably small [37].…”

Section: Tp Flow Schedulingmentioning

confidence: 99%

Practicalizing Delay-Tolerant Mobile Apps with Cedos

Moon

Kim

et al. 2015

Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services

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Delay-tolerant Wi-Fi offloading is known to improve overall mobile network bandwidth at low delay and low cost. Yet, in reality, we rarely find mobile apps that fully support opportunistic Wi-Fi access. This is mainly because it is still challenging to develop delay-tolerant mobile apps due to the complexity of handling network disruptions and delays.In this work, we present Cedos, a practical delay-tolerant mobile network access architecture in which one can easily build a mobile app. Cedos consists of three components. First, it provides a familiar socket API whose semantics conforms to TCP while the underlying protocol, D 2 TP, transparently handles network disruptions and delays in mobility. Second, Cedos allows the developers to explicitly exploit delays in mobile apps. App developers can express maximum user-specified delays in content download or use the API for real-time buffer management at opportunistic Wi-Fi usage. Third, for backward compatibility to existing TCPbased servers, Cedos provides D 2 Prox, a protocol-translation Web proxy. D 2 Prox allows intermittent connections on the mobile device side, but correctly translates Web transactions with traditional TCP servers. We demonstrate the practicality of Cedos by porting mobile Firefox and VLC video streaming client to using the API. We also implement delay/disruption-tolerant podcast client and run a field study with 50 people for eight weeks. We find that up to 92.4% of the podcast traffic is offloaded to Wi-Fi, and one can watch a streaming video in a moving train while offloading 48% of the content to Wi-Fi without a single pause.

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Section: Tp Flow Schedulingmentioning

confidence: 99%

Practicalizing Delay-Tolerant Mobile Apps with Cedos

Moon

Kim

et al. 2015

Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services

Self Cite

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“…This latency may be partially compensated for by a faster task execution time at the cloud server. The basic tradeoffs involving these attributes and how they relate to the decision to offload task execution have been studied extensively (Wu and Wolter (2017); Mehmeti and Spyropoulos (2016); Zhang and Cao (2018); Zhang et al (2015); Kim et al (2016)).…”

Section: Related Workmentioning

confidence: 99%

Efficient Mobile Computation Offloading with Hard Task Deadlines and Concurrent Local Execution

Teymoori

Todd

Zhao

et al. 2020

GLOBECOM 2020 - 2020 IEEE Global Communications Conference

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This work considers the problem of mobile computation offloading over stochastic wireless communication channels. The objective is to minimize the energy consumption of the mobile device while satisfying a hard task execution deadline. To guarantee that hard task execution deadline constraints are met, a model of concurrent local execution (CLE) is introduced, where local task execution may be initiated even if remote offloading is in progress. This problem is addressed for continuous (1-Part), multi-part (K-Part), and preemptive offloading scenarios. The theory of optimal stopping for Markov chains and dynamic programming are used to develop provably optimal online offloading algorithms for each offloading scenario. The computational complexity of these algorithms, especially in the case of preemptive offloading, can be restrictive. Some approximation techniques are proposed to reduce the computational complexity of the online algorithms.

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“…Wireless signals (Wi-Fi) generated by smartphones are detected by sensors in the store. In a recent study related to mobile devices, the Wi-Fi activation index ratio is approximately 48.2% [34,35]. Therefore, assuming most visiting customers have mobile phones, about half of all mobile devices and their MAC addresses could be detected.…”

Section: Location-based Tracking Technologymentioning

confidence: 99%

Location-Based Tracking Data and Customer Movement Pattern Analysis for Sustainable Fashion Business

et al. 2019

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Retailers need accurate movement pattern analysis of human-tracking data to maximize the space performance of their stores and to improve the sustainability of their business. However, researchers struggle to precisely measure customers’ movement patterns and their relationships with sales. In this research, we adopt indoor positioning technology, including wireless sensor devices and fingerprinting techniques, to track customers’ movement patterns in a fashion retail store over four months. Specifically, we conducted three field experiments in three different timeframes. In each experiment, we rearranged one element of the visual merchandising display (VMD) to track and compare customer movement patterns before and after the rearrangement. For the analysis, we connected customers’ discrete location data to identify meaningful patterns in customers’ movements. We also used customers’ location and time information to match identified movement pattern data with sales data. After classifying individuals’ movements by time and sequences, we found that stay time in a particular zone had a greater impact on sales than the total stay time in the store. These results challenge previous findings in the literature that suggest that the longer customers stayed in a store, the more they purchase. Further, the results confirmed that effective store rearrangement could change not only customer movement patterns but also overall sales of store zones. This research can be a foundation for various practical applications of tracking data technologies.

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Multi-flow rate control in delayed Wi-Fi offloading systems

Cited by 7 publications

References 13 publications

Practicalizing Delay-Tolerant Mobile Apps with Cedos

Practicalizing Delay-Tolerant Mobile Apps with Cedos

Efficient Mobile Computation Offloading with Hard Task Deadlines and Concurrent Local Execution

Location-Based Tracking Data and Customer Movement Pattern Analysis for Sustainable Fashion Business

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