With the goal of developing low-cost and innovative perception and localization techniques for autonomous vehicles, this work explores a system that solely relies on a LoRa receiver and a magnetometer for agent localization within urban environments. Using the received signal strength from LoRa beacons distributed across a test area of 16,000 square meters, a model of expected RSSI values per beacon is estimated using Gaussian Process (GP) regression. Motion is estimated using a probabilistic signal similarity classifier, and localization is obtained via a particle filter. Our experiments demonstrate that our proposed system is able to estimate our location to within three meters RMSE when the agent is within the convex hull of prior data. In real-world scenarios, characterized by signal interference and environmental complexities, our approach highlights the potential of leveraging affordable technology such as LoRa receivers and magnetometers for robust and accurate location estimation in complex urban environments. The integration of low-cost LoRa devices, Gaussian Process regression, particle filtering and our novel signal similarity motion estimator offers a promising avenue for achieving cost-effective localization solutions without compromising accuracy or reliability.

IEEE 802.11 (WiFi) only has two modes of trust---complete trust or complete untrust. The lack of nuance leaves no room for sensors that a user does not fully trust but wants to connect to their network, such as a WiFi sensor. Solutions exist, but they require advanced knowledge of network administration. We solve this problem by introducing a new way of modulating data in the latency of the network, called Latency Shift Keying. We use specific characteristics of the WiFi protocol to carefully control the latency of just one device on the network. We build a transmitter, receiver, and modulation scheme that is designed to encode data in the latency of a network. We develop an application, Wicket, that solves the WiFi trust issue using Latency Shift Keying to create a new security association between an untrusted WiFi sensor and a wired device on the trusted network. We evaluate its performance and show that it works in many network conditions and environments.

Community-driven sensor networks have been instrumental in providing easy access to affordable, large-scale measurement recording, facilitated by the accessibility of inexpensive sensor hardware. The simplicity of this hardware makes it challenging to retrieve trustworthy location data without added hardware such as GPS. We introduce the LaMDA framework, a software-based solution run solely in a web browser to determine the location of a device with the aid of a registering device. We monitor the device for any location changes by analyzing its traceroute data from a central server. Our solution allows minimal firmware changes to be made to fleets of devices without recall or changes to hardware.

We design and build a protocol called on-off noise power communication (ONPC), which modifies the software in commodity packet radios to allow communication, independent of their standard protocol, at a very slow rate at long range. To achieve this long range, we use the transmitter as an RF power source that can be on or off if it does or does not send a packet, respectively, and a receiver that repeatedly measures the noise and interference power level. We use spread spectrum techniques on top of the basic on/off mechanism to overcome the interference caused by other devices' channel access to provide long ranges at a much lower data rate. We implement the protocol on top of commodity WiFi hardware. We discuss our design and how we overcome key challenges such as non-stationary interference, carrier sensing and hardware timing delays. We test ONPC in several situations to show that it achieves significantly longer range than standard WiFi.

When several internet-of-things devices are required to be installed in a smart home, significant effort is required to provide each device with the association information for the home's wireless router. We design and build a novel protocol called Secure Transfer of Association Protocol (STRAP), which securely bootstraps connectivity between a set of deployed WiFi devices and a home's wireless router. We show that STRAP works in a variety of environments and is faster than conventional methods for connecting WiFi devices to home wireless routers.

Using an air purifying system can remove indoor air pollutants, but because it increases electric power utilization, results in broader increases in air pollution. To explore the tradeoff between energy consumption and healthful air, we demonstrate the indoor air sensing and automation (IASA) system, an internet-of-things system. The IASA system uses an air quality sensor, gateway device, and smart thermostat to control the fan in a home's heating and cooling system. When fine particulate matter is high, the system operates the fan to pull air through a furnace filter and remove the pollution from the indoor air. We describe our system design, deployment, and collected data. To date, we have collected 861,000 air quality measurements with IASA.

Device-free localization (DFL) systems locate a person in an environment by measuring the changes in received signals on links in a wireless network. A fingerprint-based DFL method collects a training database of measurement fingerprints and uses a machine learning classifier to determine a person's location from a new fingerprint. However, as the environment changes over time due to furniture or other objects being moved, the fingerprints diverge from those in the database. This paper addresses, for DFL methods that use received signal strength as measurements, the degradation caused as a result of environmental changes. We perform experiments to quantify how changes in an environment affect accuracy, through a repetitive process of randomly moving an item in a residential home and then conducting a localization experiment, and then repeating. We quantify the degradation and consider ways to be more robust to environmental change. We find that the localization error rate doubles, on average, for every six random changes in the environment. We find that the random forests classifier has the lowest error rate among four tested. We present a correlation method for selecting channels, which decreases the localization error rate from 4.8% to 1.6%.

Extensive research has been performed on improving TCP performance in multi-hop wireless networks, but there have been relatively few experimental evaluations of this work. To make it easier to conduct research in this area, we are releasing WiFu, an open-source toolkit for developing experimental wireless transport protocols. WiFu provides for user-space development of reliable transport and rate control algorithms, greatly simplifying the implementation effort required. In this paper, we describe the architecture of the WiFu toolkit, which decomposes transport protocols into smaller components that enable rapid, plug-and-play development of new variants. We present experiments to demonstrate that the performance of WiFu compares favorably to the Linux kernel for wireless networks. We illustrate the utility of WiFu by using it to conduct experiments with several wireless transport protocols, and show that the performance of some protocols differs significantly from previously reported results.

The FPGA compilation process (synthesis, map, place, and route) is a time consuming task that severely limits designer productivity. Compilation time can be reduced by saving implementation data in the form of hard macros. Hard macros consist of previously synthesized, placed and routed circuits that enable rapid design assembly because of the native FPGA circuitry (primitives and nets)which they encapsulate. This work presents results from creating a new FPGA design flow based on hard macros called HMF low. HMF low has shown speedups of 10-50X over the fastest configuration of the Xilinx tools. Designed for rapid prototyping, HMF low achieves these speedups by only utilizing up to 50 percent of the resources on an FPGA and produces implementations that run 2-4X slower than those produced by Xilinx. These speedups are obtained on a wide range of benchmark designs with some exceeding 18,000 slices on a Virtex 4 LX200.

Creating CAD tools for commercial FPGAs is a difficult task. Closed proprietary device databases and unsupported interfaces are largely to blame for the lack of CAD research found on commercial architectures versus hypothetical architectures. This paper formally introduces RapidSmith, a new set of tools and APIs that enable CAD tool creation for Xilinx FPGAs. Based on the Xilinx Design Language (XDL), RapidSmith provides a compact, yet, fast device database with hundreds of APIs that enable the creation of placers, routers and several other tools for Xilinx devices. RapidSmith alleviates several of the difficulties of using XDL and this work demonstrates the kinds of research facilitated by removing such challenges.

Designer productivity for FPGA design is significantly limited by the time-consuming nature of the FPGA compilation process (synthesis, map, placement, and routing). However, experimentation on alternative CAD tools for this purpose for Xilinx devices has been somewhat limited. This paper describes the development and distribution of RapidSmith, a software library to facilitate the manipulation of XDL designs and upon which a complete CAD system can be based. The demonstration portion of this paper will show prototypes of representative CAD tools which can be easily built on top of the RapidSmith system.