Paul T. Congdon

Avoiding multipath to revive inbuilding WiFi localization

Despite of several years of innovative research, indoor localization is still not mainstream. Existing techniques either employ cumbersome fingerprinting, or rely upon the deployment of additional infrastructure. Towards a solution that is easier to adopt, we propose CUPID, which is free from these restrictions, yet is comparable in accuracy. While existing WiFi based solutions are highly susceptible to indoor multipath, CUPID utilizes physical layer (PHY) information to extract the signal strength and the angle of only the direct path, successfully avoiding the effect of multipath reflections. Our main observation is that natural human mobility, when combined with PHY layer information, can help in accurately estimating the angle and distance of a mobile device from an wireless access point (AP). Real-world indoor experiments using off-the-shelf wireless chipsets confirm the feasibility of CUPID. In addition, while previous approaches rely on multiple APs, CUPID is able to localize a device when only a single AP is present. When a few more APs are available, CUPID can improve the median localization error to 2.7m, which is comparable to schemes that rely on expensive fingerprinting or additional infrastructure.

Hey, you darned counters!: get off my ASIC!

Software-Defined Networking (SDN) gains much of its value through the use of central controllers with global views of dynamic network state. To support a global view, SDN protocols, such as OpenFlow, expose several counters for each flow-table rule. These counters must be maintained by the data plane, which is typically implemented in hardware as an ASIC. ASIC-based counters are inflexible, and cannot easily be modified to compute novel metrics. These counters do not need to be on the ASIC. If the ASIC data plane has a fast connection to a general-purpose CPU with cost-effective memory, we can replace traditional counters with a stream of rule-match records, transmit this stream to the CPU, and then process the stream in the CPU. These software-defined counters allow far more flexible processing of counter-related information, and can reduce the ASIC area and complexity needed to support counters.

Asymmetric caching: improved network deduplication for mobile devices

Network deduplication (dedup) is an attractive approach to improve network performance for mobile devices. With traditional deduplication, the dedup~source uses only the portion of the cache at the dedup~destination that it is aware of. We argue in this work that in a mobile environment, the dedup~destination (say the mobile) could have accumulated a much larger cache than what the current dedup~source is aware of. This can occur because of several reasons ranging from the mobile consuming content through heterogeneous wireless technologies, to the mobile moving across different wireless networks. In this context, we propose asymmetric caching, a solution that is overlaid on baseline network deduplication, but which allows the dedup~destination to selectively feedback appropriate portions of its cache to the dedup~source with the intent of improving the redundancy elimination efficiency. We show using traffic traces collected from 30 mobile users, that with asymmetric caching, over 89% of the achievable redundancy can be identified and eliminated even when the dedup~source has less than one hundredth of the cache size as the dedup~destination. Further, we show that the ratio of bytes saved from transmission at the dedup~source because of asymmetric caching is over 6x that of the number of bytes sent as feedback. Finally, with a prototype implementation of asymmetric caching on both a Linux laptop and an Android smartphone, we demonstrate that the solution is deployable with reasonable CPU and memory overheads.

Simultaneously reducing latency and power consumption in openflow switches

The Ethernet switch is a primary building block for todays enterprise networks and data centers. As network technologies converge upon a single Ethernet fabric, there is ongoing pressure to improve the performance and efficiency of the switch while maintaining flexibility and a rich set of packet processing features. The OpenFlow architecture aims to provide flexibility and programmable packet processing to meet these converging needs. Of the many ways to create an OpenFlow switch, a popular choice is to make heavy use of ternary content addressable memories (TCAMs). Unfortunately, TCAMs can consume a considerable amount of power and, when used to match flows in an OpenFlow switch, put a bound on switch latency. In this paper, we propose enhancing an OpenFlow Ethernet switch with per-port packet prediction circuitry in order to simultaneously reduce latency and power consumption without sacrificing rich policy-based forwarding enabled by the OpenFlow architecture. Packet prediction exploits the temporal locality in network communications to predict the flow classification of incoming packets. When predictions are correct, latency can be reduced, and significant power savings can be achieved from bypassing the full lookup process. Simulation studies using actual network traces indicate that correct prediction rates of 97% are achievable using only a small amount of prediction circuitry per port. These studies also show that prediction circuitry can help reduce the power consumed by a lookup process that includes a TCAM by 92% and simultaneously reduce the latency of a cut-through switch by 66%.

MCNet: Crowdsourcing wireless performance measurements through the eyes of mobile devices

Measurement of network performance in complex WiFi networks, such as in corporations and universities, is an important but challenging task, as wireless performance in networks with many access points is hard to measure and model. We demonstrate that crowdsourcing the task of measuring WiFi performance is an effective solution to this problem. By measuring performance directly with unmodified consumer mobile devices such as smartphones, it is possible to cheaply and easily detect problems that matter to users. Aggregated performance data across clients can then provide a global view of performance trends in an enterprise network. We demonstrate that periodic sampling allows the collection of representative data while keeping battery consumption low, and we leverage mobile sensor information to intelligently schedule these measurements. This system was deployed in two different large WLANs, where we discovered numerous previously undetected performance problems.

On cloud-centric network architecture for multi-dimensional mobility

Despite pervasive deployment of wireless networks, maintaining seamless mobile connectivity within a set of local devices and to the remote cloud is still challenging. The crux of this challenge stems from the simultaneous interplay of multiple dimensions of a users mobility - users frequently move between multiple access networks, mobile devices and unique personas. We identify new trends and challenges in providing rich mobile connectivity to mobile users. We then propose a novel Cloud-centric Architecture for Rich Mobile Experience Networking, called Carmen. Carmen is a distributed system that manages the mobile connectivity of a set of devices belonging to a particular individual, which we call the mobile personal grid (MPG). Carmen enables the MPG to efficiently collect context from a mobile user and coordinate key system resources across the MPG and cloud. We present new design principles and functional components of Carmen. In addition, we show our system prototype of Carmens resource monitoring infrastructure to demonstrate its feasibility and benefits in improving the mobile users networking experience.

An adaptive privacy-preserving scheme for location tracking of a mobile user

Many popular mobile applications require the continuous monitoring and sharing of a mobile users location. However, exploiting a users location leads to disclosing sensitive information about the users daily activity. Several location privacy-preserving schemes have been proposed, but it remains challenging for a user to achieve visibility of the associated threats as well as to control the impact of those threats. This paper presents an adaptive location privacy-preserving system (ALPS) that allows for a user to control the level of privacy disclosure with different quality of location-based service (LBS). We have identified key attack models on location tracking using powerful map-matching algorithms, and then defined a scheme that allows a user to control the privacy of tracking information. We have implemented ALPS on Android OS and evaluated the implementation extensively via trace-based simulation, showing the effectiveness of user-controllable privacy preservation.

Packet prediction for speculative cut-through switching

The amount of intelligent packet processing in an Ethernet switch continues to grow, in order to support of embedded applications such as network security, load balancing and quality of service assurance. This increased packet processing is contributing to greater per-packet latency through the switch. In addition, there is a growing interest in using Ethernet switches in low latency environments such as high-performance clusters, storage area networks and real-time media distribution. In this paper we propose Packet Prediction for Speculative Cut-through Switching (PPSCS), a novel approach to reducing the latency of modern Ethernet switches without sacrificing feature rich policy-based forwarding enabled by deep packet inspection. PPSCS exploits the temporal nature of network communications to predict the flow classification of incoming packets and begin the speculative forwarding of packets before complex lookup operations are complete. Simulation studies using actual network traces indicate that correct prediction rates of up to 97% are achievable using only a small amount of prediction circuitry per port. These studies also indicate that PPSCS can reduce the latency in traditional store-and-forward switches by nearly a factor of 8, and reduce the latency of cut-through switches by a factor of 3.