Screen-off traffic characterization and optimization in 3G/4G networks

Huang, Junxian; Qian, Feng; Mao, Z. Morley; Sen, Subhabrata; Spatscheck, Oliver

doi:10.1145/2398776.2398813

Cited by 66 publications

(42 citation statements)

References 16 publications

(16 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have the two following challenges to solve. First, background applications during crowd events are delay-sensitive (such as gathering group background management processes in screen-off traffic [9]). Hence, delaying client's traffic should not sacrifice the packet delay performance.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Exploiting Delay-Aware Load Balance for Scalable 802.11 PSM in Crowd Event Environments

Wei

Chen

Xia

et al. 2017

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

This paper presents ScaPSM (i.e., Scalable Power-Saving Mode Scheduler), a design that enables scalable competing background traffic scheduling in crowd event 802.11 deployments with Power-Saving Mode (PSM) radio operation. ScaPSM prevents the packet delay proliferation of previous study, if applied in the crowd events scenario, by introducing a new strategy of adequate competition among multiple PSM clients to optimize overall energy saving without degrading packet delay performance. The key novelty behind ScaPSM is that it exploits delay-aware load balance to control judiciously the qualification and the number of competing PSM clients before every beacon frame's transmission, which helps to mitigate congestion at the peak period with increasing the number of PSM clients. With ScaPSM, the average packet delay is bounded and fairness among PSM clients is simultaneously achieved. ScaPSM is incrementally deployable due to only AP-side changes and does not require any modification to the 802.11 protocol or the clients. We theoretically analyze the performance of ScaPSM. Our experimental results show that the proposed design is practical, effective, and featuring with significantly improved scalability for crowd events.

show abstract

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Exploiting Delay-Aware Load Balance for Scalable 802.11 PSM in Crowd Event Environments

Wei

Chen

Xia

et al. 2017

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…We then look at more fine-grained intermittent usage activity (such as ON-OFF usage) in Section 3.2. Note that for all our analysis we consider the network usage as an indication of device usage, as it is known that the maximum network traffic volume is created when a device screen is in [Huang et al 2012]. We also consider all the packets created by the devices (including TCP control packets, etc.).…”

Section: Multi-device Utilizationmentioning

confidence: 99%

Characterization of Wireless Multidevice Users

Das

Pathak

Chuah

et al. 2016

ACM Trans. Internet Technol.

View full text Add to dashboard Cite

The number of wireless-enabled devices owned by a user has had a huge growth over the last few years. Over one third of adults in the United States currently own three wireless devices -smartphone, laptop and tablet. This paper provides a study of the network usage behavior of today's multi-device users. Using a data collected from a large university campus, we provide a detailed multi-device user (MDU) measurement study of over 30,000 users. The major objective of this work is to study how the presence of multiple wireless devices affect the network usage behavior of users. Specifically, we characterize the usage pattern of the the different device types in terms to total and intermittent usage, how the usage of different devices overlap over time and uncarried device usage statistics. We also study user's preferences of accessing sensitive content and device-specific factors that govern the choice of WiFi encryption type. The study reveals several interesting findings about multi-device users. We see how the usage of tablet and laptops are inter-changeable, how the overall multi-device usage is additive instead of being shared among the devices. We also observe how current DHCP configurations are oblivious to multiple devices which result in inefficient utilization of available IP address space. All the findings about multi-device usage patterns has the potentiality to be utilized by different entities, like app developers, network providers, security researchers, analytics and advertisement systems, to provide more intelligent and informed services to users who have at least two among smartphones, tablets and laptops.

show abstract

“…Huge energy savings are reported for power measurements for cellular radio/LTE traffic obtained during screen-off conditions [15], Wi-Fi [9,16,24,25]. In PRiSM, we perform measurements under both screen on and off conditions, show that screen off energy is more compared to screen on due to use of CPU wakelocks, and use appropriate energy values for user logs.…”

Section: Screen Activationmentioning

confidence: 99%

Wi-Fi Hotspot Auto-Discovery: A Practical & Energy-Aware System for Smart Objects using Cellular Signals

Poosamani¹,

Rhee²

2015

Proceedings of the 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

View full text Add to dashboard Cite

The Internet of Things (IoT) paradigm aims to interconnect a variety of heterogeneous Smart Objects (SO) using energy-efficient methodologies and standard communication protocols. A majority of consumer devices sold today come equipped with wireless LAN and cellular technology to connect with the world-wide network. To discover Wi-Fi hot spots, there is a need for constant scanning of Wi-Fi radio in these devices and results in significant battery drain. We present PRiSM, a practical system to automatically locate Wi-Fi hotspots while Wi-Fi radio is turned off, by using the statistical characteristics of cellular signals. Cellular signals are received at zero extra cost in mobile devices and hence PRiSM is highly energy-efficient. It is a lightweight client-side only implementation and needs no prior knowledge on floor plans or wireless infrastructure. We implement PRiSM on Android-based devices and show up to 96% of energy savings in Wi-Fi sensing operations which is equivalent to saving up to 16% of total battery capacity, together with an average prediction accuracy of up to 98%.

show abstract

Screen-off traffic characterization and optimization in 3G/4G networks

Cited by 66 publications

References 16 publications

Exploiting Delay-Aware Load Balance for Scalable 802.11 PSM in Crowd Event Environments

Exploiting Delay-Aware Load Balance for Scalable 802.11 PSM in Crowd Event Environments

Characterization of Wireless Multidevice Users

Wi-Fi Hotspot Auto-Discovery: A Practical & Energy-Aware System for Smart Objects using Cellular Signals

Contact Info

Product

Resources

About