Recent studies have shown that mobile applications are increasingly becoming "chatty", resulting in excessive signaling load on cellular networks, potentially causing major network outages. One of the main causes of this signaling overload is the static dormancy timer that 3GPP-based networks employ to control the release of radio resources allocated to a device. To systematically manage the ever-increasing signaling load, we design a novel network-aware system named REACT for dynamically tuning the dormancy timers. In doing so, we highlight a key trade-off between signaling overhead on the network and energy consumed on mobile devices. Contrary to the prior work, our solution models and predicts traffic of mobile apps at the network side with high degree of accuracy, and then dynamically tunes the dormancy timer at a per-device, per-base-station or per-RNC granularity, the approach requires no modifications on mobile devices. Our evaluation shows that REACT reduces signaling overhead by more than a factor of 2 compared to current static setting of dormancy timers. Furthermore, network-aware dynamic tuning of dormancy timers enables a flexible system that can aggressively save energy on devices when the overall network load is low, and manages signaling overhead effectively during high network load.

It is well-known that the performance of Web-browsing as well as mobile applications (or apps) suffers on today's cellular networks. In this work, we perform a systematic measurement study of more than 50 popular apps and 2 cellular networks, and discover that while cellular networks have predictable latency, it is the path between exit points of cellular networks (e.g., GGSN) and cloud-servers that degrades apps performance. High latency and unpredictability over this path affects browsing and activity completion times of apps, worsening the performance by several magnitudes. Furthermore, we find that as the number of apps on mobile devices increases, cellular networks in turn suffer due to large number of active connections, primarily used for push notifications, experiencing heavy signaling overhead in the network. Towards accelerating the performance of apps and improving their operational efficiency, we envision an easy to deploy operator-managed platform, and study two architectural optimizations that sit at vantage points inside cellular networks: virtual app-server (vApp) and network-assisted, virtual push-notification server (vPNS). vApps improve apps' browsing experience while vPNSs take the burden of carrying periodic message off cellular networks. Using trace-driven simulations, we find that vApps can improve activity completion times by more than 3-fold, whereas vPNS can reduce the signaling load by a factor of 6 in cellular networks and reduce energy consumption by a factor of 2 on mobile devices.

We design and implement a novel system called Async, which enables a mobile network operator (MNO) to efficiently manage the growth of mobile data by leveraging the delay-elastic nature of certain applications and the price-sensitive nature of certain users. Specifically, Async introduces an alternate “asynchronous” content-delivery paradigm for heavy content (e.g., videos), and facilitates an MNO to negotiate with users a delay in delivery in exchange for appropriate incentives. The MNO uses the negotiated delays to actively manage Async flows to reduce congestion and improve the quality-of-experience (QoE) of both delayed and regular flows. We show that in comparison to state-of-the-art, Async's network-based flow management enhances QoE for more than 30% of the regular flows, with up to 60% improvement in per-flow QoE metric, while still meeting the negotiated delivery times of 95% of the delayed flows. Async also lowers the delivery times of delayed flows by ∼67% and significantly increases robustness to traffic unpredictability. Our design is robust to disconnections and does not require any modifications to existing network infrastructure and protocols. Our prototype deployment (using Apache's mod_proxy and an Android app) on live networks confirms Async's efficacy in meeting EDTs for diverse deployment scenarios.

In this work, we demonstrate the design and implementation of a novel system called Async, which enables a mobile network operator (MNO) to efficiently manage the growth of mobile data. For this, we enable an opportunistic delivery model for the MNOs by leveraging the delay-elastic nature of certain applications such as video downloads, and the price-sensitive nature of certain users. Specifically, in Async, we enable an alternate content delivery paradigm that facilitates an MNO to control when, and at what price, the content is delivered to the users. In our system, users can select a price and expected delivery time (or EDT) option from a multitude of choices. We develop a prototype of such a system by extending Apache's mod_proxy module and by developing a service on the Android platform without requiring any modifications to existing network infrastructure or protocols. We demonstrate two applications using such a service: (1) download of large files, e.g., software updates, movie files, and (2) an educational App that downloads educational content on mobile devices.

The Internet is witnessing a rapid increase in video traffic. Due to the scalability and the cost-savings offered by cloud-computing, Internet video service providers are increasingly delivering their content from multi-tenant cloud data centers. One of the major challenges faced by such a video service provider is the management of the Quality-of-Experience (QoE) of the end-users in the presence of Variable Bit Rate (VBR) video flows, time varying network conditions in the Internet, and the bounded egress bandwidth provided by the data center. To this end, we present InSite, a light-weight and easy-to-deploy solution for managing the QoE of a set of video flows of a service provider, which are served from a data center. InSite is deployed at the egress of a data center, between the video servers and the clients, and manages the video flows that are transmitted over TCP. The solution uses a novel generalized binary search technique to concurrently search for the appropriate flow rates for a set of flows, with the goal of maximizing the QoE-fairness across the flows, as opposed to TCP-fairness. The search takes into account the total egress bandwidth allocated for the set of video flows at the data center, the unknown and possibly time-varying capacities of any remote bottleneck links, and the playout buffer sizes of the video flows. The solution is also designed to operate with minimal modifications to the video servers and the clients. In our evaluations using extensive ns-3 simulations and a testbed implementation for serving videos over TCP, we observe that deploying InSite achieves several folds reduction in playout stalls over a system without InSite.

In this article, we consider the goal of achieving high throughput in a wireless sensor network. Our work is set in the context of those wireless sensor network applications which collect and transfer bulk data. We present PIP (Packets in Pipe), a MAC primitive for use by the transport module to achieve high throughput. PIP has a unique set of features: (a) it is a multihop connection-oriented primitive, (b) it is TDMA based, (c) it uses multiple radio channels, and (d) it is centrally controlled. This represents a significant shift from prior MAC protocols for bulk data transfer. PIP has several desirable properties: (a) its throughput degrades only slightly with increasing number of hops, (b) it is robust to variable wireless error rates, (c) it performs well even without any flow control, and (d) requires only small queue sizes to operate well. We substantiate these properties with a prototype implementation of PIP on the Tmote-Sky CC2420-based platform. PIP achieves about eleven times better throughput than the state-of-the-art prior work, over a network depth of 24 hops. We also show that PIP can be interagted with duty cycling, and that PIP can support streaming data from/to flash at little overhead.

In this paper, we consider the goal of achieving high throughput in a wireless sensor network. Our work is set in the context of those wireless sensor network applications which collect and transfer bulk data. We present PIP (Packets in Pipe), a MAC primitive for use by the transport module to achieve high throughput. PIP has a unique set of features: (a) it is a multi-hop connection oriented primitive, (b) it is TDMA-based, (c) it uses multiple radio channels, and (d) it is centrally controlled. This represents a significant shift from prior MAC protocols for bulk data transfer.

In this work, we consider the goal of enabling effective voice communication in a TDMA, multi-hop mesh network, using low cost and low power platforms. We consider two primary usage scenarios: (1) enabling a local voice communication within a village-like setting, in developing regions (2) supporting an on-site local communication among a team of users e.g. during emergency response systems.