Chit-Kwan Lin

Determining RF angle of arrival using COTS antenna arrays: A field evaluation

We are interested in estimating the angle of arrival of an RF signal by using commercial-off-the-shelf (COTS) software-defined radios (SDRs). The proposed COTS-based approach has the advantages of flexibility, low cost and ease of deployment, but-unlike traditional phased antenna arrays in which elements are already phase-aligned - we face the challenge of aligning individual SDRs during field deployment in order to ensure coherent phase detection. We propose a strategy to relax the requirement of tight phase synchronization between distributed oscillators by using a novel phase difference of arrival mechanism based on a field-deployable reference transmitter. This approach enables flexible and inexpensive COTS phased-array designs. We evaluate our method in an outdoor, 20m×20m open field and observe localization errors below 3m. We conclude that a COTS-based approach to RF source localization is amenable to rapid and low-cost deployment of sensing infrastructure and could potentially be of interest to the Intelligence, Surveillance and Reconnaissance (ISR) community at the tactical edge.

A location-dependent runs-and-gaps model for predicting TCP performance over a UAV wireless channel

In this paper, we use a finite-state model to predict the performance of the Transmission Control Protocol (TCP) over a varying wireless channel between an unmanned aerial vehicle (UAV) and ground nodes. As a UAV traverses its flight path, the wireless channel may experience periods of significant packet loss, successful packet delivery, and intermittent reception. By capturing packet run-length and gap-length statistics at various locations on the flight path, this location-dependent model can predict TCP throughput in spite of dynamically changing channel characteristics. We train the model by using packet traces from flight tests in the field and validate it by comparing TCP throughput distributions for model-generated traces against those for actual traces randomly sampled from field data. Our modeling methodology is general and can be applied to any UAV flight path.

Rainbow: A wireless medium access control using network coding for multi-hop content distribution

We consider the problem of multi-hop content distribution over a wireless ad-hoc network. Such mechanisms are relevant to a broad spectrum of applications, but are particularly important to data broadcast in wireless distributed computing where speedy I/O is critical to overall performance. In this paper, we present Rainbow, a content distribution protocol for multi-hop wireless ad-hoc networks. The protocol uses a content-directed medium access control (MAC), through which transmission priority is given to those nodes most capable of delivering useful content to their neighbors. We describe an efficient implementation of Rainbow based on network coding. Specifically, Rainbow uses a MAC priority scheme, where the priority of packet transmission from a node depends on the rank of the coefficient matrix associated with the coded content the node holds. We demonstrate that Rainbow achieves a 1.3-to 1.9-fold improvement in content distribution time over other flooding protocols, as measured on a testbed of 29 wireless nodes. We attribute this performance gain in part to Rainbowpsilas ability to address a MAC-level bottleneck in multi-hop wireless networks, which we refer to as the ldquobridge lock-out problemrdquo.

Achieving High Throughput Ground-to-UAV Transport via Parallel Links

Wireless data transfer under high mobility, as found in unmanned aerial vehicle (UAV) applications, is a challenge due to varying channel quality and extended link outages. We present FlowCode, an easily deployable link-layer solution utilizing multiple transmitters and receivers for the purpose of supporting existing transport protocols such as TCP in these scenarios. By using multiple transmitters and receivers and by exploiting the resulting antenna beam diversity and parallel transmission effects, FlowCode increases throughput and reception range. In emulation, we show that TCP over FlowCode gives greater goodput over a larger portion of the flight path, compared to an enhanced TCP protocol using the standard 802.11 MAC. In the process, we make a strong case for using trace-modulated emulation when developing distributed protocols for complex wireless environments.

Measuring diversity on a low-altitude UAV in a ground-to-air wireless 802.11 mesh network

We consider the problem of mitigating a highly varying wireless channel between a transmitting ground node and receivers on a small, low-altitude unmanned aerial vehicle (UAV) in a 802.11 wireless mesh network. One approach is to use multiple transmitter and receiver nodes that exploit the channels spatial/temporal diversity and that cooperate to improve overall packet reception. We present a series of measurement results from a real-world testbed that characterize the resulting wireless channel. We show that the correlation between receiver nodes on the airplane is poor at small time scales so receiver diversity can be exploited. Our measurements suggest that using several receiver nodes simultaneously can boost packet delivery rates substantially. Lastly, we show that similar results apply to transmitter selection diversity as well.

Speculative pipelining for compute cloud programming

MapReduce job execution typically occurs in sequential phases of parallel steps. These phases can experience unpredictable delays when available computing and network capacities fluctuate or when there are large disparities in inter-node communication delays, as can occur on shared compute clouds. We propose a pipeline-based scheduling strategy, called speculative pipelining, which uses speculative prefetching and computing to minimize execution delays in subsequent stages due to varying resource availability. Our proposed method can mask the time required to perform speculative operations by overlapping with other ongoing operations. We introduce the notion of “open-option” prefetching, which, via coding techniques, allows speculative prefetching to begin even before knowing exactly which input will be needed. On a compute cloud testbed, we apply speculative pipelining to the Hadoop sorting benchmark and show that sorting time is shortened significantly.

FlowCode: Multi-site data exchange over wireless ad-hoc networks using network coding

We present FlowCode, a system that exploits network coding at the granularity of traffic flows to facilitate fault-tolerant data exchange in wireless mesh networks. Applications include multi-site data replication in ad-hoc environments such as mesh networks or wireless data centers. By coupling an operand-driven transmission mechanism with a layered network topology, FlowCode allows us to realize the gains of network coding in application systems without a global scheduler. We analyze the resulting gains through modeling and simulation and validate our results on an outdoor testbed of 12 wireless devices. Results indicate that in high loss environments, FlowCode provides the most significant gains from improved fault tolerance over redundant paths.

A computational wireless network backplane: Performance in a distributed speaker identification application

A major challenge in the DoDpsilas next-generation network-centric information systems concerns on-demand provisioning of computation and network infrastructures at tactical network edges (e.g., deploying wireless airborne or hybrid air/ground networks). To support this vision, we present DWARF, a general distributed application execution framework for wireless ad-hoc networks which dynamically allocates computation resources and manages failures. DWARF nodes each run a separate task simultaneously, thereby achieving execution speed-up from parallel processing. Failed tasks, e.g., due to fluctuating wireless links to mobile nodes, are automatically detected and reassigned, transparent to the application. Further, tasks are executed in an order that satisfies dependencies given by task dependency graphs. To use DWARF, application programmers need only decompose their applications into tasks and define the task dependency graphs. In this paper, we describe DWARF and report its benefits in running an important existing application-speaker identification-over a 32-node wireless network which supports fault-tolerant computation. We observed two major performance gains: (1) a ten-fold speed-up in identifying speakers due to parallelizing the application, and (2) higher accuracy in speaker identification, made possible by the increased sensor diversity provided by geographically distributed nodes. While our nodes have modest computing power individually, combined under DWARF, they are able to execute speaker identification with much greater speed and with improved accuracy.