Chieh-Jan Mike Liang

Surviving wi-fi interference in low power ZigBee networks

Frequency overlap across wireless networks with different radio technologies can cause severe interference and reduce communication reliability. The circumstances are particularly unfavorable for ZigBee networks that share the 2.4 GHz ISM band with WiFi senders capable of 10 to 100 times higher transmission power. Our work first examines the interference patterns between ZigBee and WiFi networks at the bit-level granularity. Under certain conditions, ZigBee activities can trigger a nearby WiFi transmitter to back off, in which case the header is often the only part of the Zig-Bee packet being corrupted. We call this the symmetric interference regions, in comparison to the asymmetric regions where the ZigBee signal is too weak to be detected by WiFi senders, but WiFi activity can uniformly corrupt any bit in a ZigBee packet. With these observations, we design BuzzBuzz to mitigate WiFi interference through header and payload redundancy. Multi-Headers provides header redundancy giving ZigBee nodes multiple opportunities to detect incoming packets. Then, TinyRS, a full-featured Reed Solomon library for resource-constrained devices, helps decoding polluted packet payload. On a medium-sized testbed, BuzzBuzz improves the ZigBee network delivery rate by 70%. Furthermore, BuzzBuzz reduces ZigBee retransmissions by a factor of three, which increases the WiFi throughput by 10%.

Design and evaluation of a versatile and efficient receiver-initiated link layer for low-power wireless

We present A-MAC, a receiver-initiated link layer for low-power wireless networks that supports several services under a unified architecture, and does so more efficiently and scalably than prior approaches. A-MACs versatility stems from layering unicast, broadcast, wakeup, pollcast, and discovery above a single, flexible synchronization primitive. A-MACs efficiency stems from optimizing this primitive and with it the most consequential decision that a low-power link makes: whether to stay awake or go to sleep after probing the channel. Todays receiver-initiated protocols require more time and energy to make this decision, and they exhibit worse judgment as well, leading to many false positives and negatives, and lower packet delivery ratios. A-MAC begins to make this decision quickly, and decides more conclusively and correctly in both the negative and affirmative. A-MACs scalability comes from reserving one channel for the initial handshake and different channels for data transfer. Our results show that: (i) a unified implementation is possible; (ii) A-MACs idle listening power increases by just 1.12x under interference, compared to 17.3x for LPL and 54.7x for RI-MAC; (iii) A-MAC offers high single-hop delivery ratios, even with multiple contending senders; (iv) network wakeup is faster and far more channel efficient than LPL; and (v) collection routing performance exceeds the state-of-the-art.

Koala: Ultra-Low Power Data Retrieval in Wireless Sensor Networks

We present Koala, a reliable data retrieval system designed to operate at permille (.1%) duty cycles, essential for long term environmental monitoring networks. Koala achieves these low duty cycles by letting the networks nodes sleep most of the time and reviving them through an efficient wake-up strategy whenever the gateway performs a bulk data download. Unlike other systems which consume energy to maintain consistent network state (e.g. routes, sleep schedules, etc.) across the networks nodes, Koala maintains no persistent routing state on the motes. Instead, a basestation calculates the network paths using reachability information collected by the motes. The flexible control protocol (FCP), a protocol we developed, is then used to install this routing information on the networks nodes. This paradigm of operation not only eliminates the overhead of maintaining routing state, but also significantly reduces the complexity of the networking code running on the motes. Results from simulation and an actual implementation on TinyOS 2 indicate that Koala can achieve very low duty cycles under a wide range of download and network sizes.

RACNet: a high-fidelity data center sensing network

RACNet is a sensor network for monitoring a data centers environmental conditions. The high spatial and temporal fidelity measurements that RACNet provides can be used to improve the data centers safety and energy efficiency. RACNet overcomes the networks large scale and density and the data centers harsh RF environment to achieve data yields of 99% or higher over a wide range of network sizes and sampling frequencies. It does so through a novel Wireless Reliable Acquisition Protocol (WRAP). WRAP decouples topology control from data collection and implements a token passing mechanism to provide network-wide arbitration. This congestion avoidance philosophy is conceptually different from existing congestion control algorithms that retroactively respond to congestion. Furthermore, WRAP adaptively distributes nodes among multiple frequency channels to balance load and lower data latency. Results from two testbeds and an ongoing production data center deployment indicate that RACNet outperforms previous data collection systems, especially as network load increases.

TOSThreads: thread-safe and non-invasive preemption in TinyOS

Many threads packages have been proposed for programming wireless sensor platforms. However, many sensor network operating systems still choose to provide an event-driven model, due to efficiency concerns. We present TOS-Threads, a threads package for TinyOS that combines the ease of a threaded programming model with the efficiency of an event-based kernel. TOSThreads is backwards compatible with existing TinyOS code, supports an evolvable, thread-safe kernel API, and enables flexible application development through dynamic linking and loading. In TOS-Threads, TinyOS code runs at a higher priority than application threads and all kernel operations are invoked only via message passing, never directly, ensuring thread-safety while enabling maximal concurrency. The TOSThreads package is non-invasive; it does not require any large-scale changes to existing TinyOS code. We demonstrate that TOSThreads context switches and system calls introduce an overhead of less than 0.92% and that dynamic linking and loading takes as little as 90 ms for a representative sensing application. We compare different programming models built using TOSThreads, including standard C with blocking system calls and a reimplementation of Tenet. Additionally, we demonstrate that TOSThreads is able to run computationally intensive tasks without adversely affecting the timing of critical OS services.

ThermoCast: a cyber-physical forecasting model for datacenters

Efficient thermal management is important in modern data centers as cooling consumes up to 50% of the total energy. Unlike previous work, we consider proactive thermal management, whereby servers can predict potential overheating events due to dynamics in data center configuration and workload, giving operators enough time to react. However, such forecasting is very challenging due to data center scales and complexity. Moreover, such a physical system is influenced by cyber effects, including workload scheduling in servers. We propose ThermoCast, a novel thermal forecasting model to predict the temperatures surrounding the servers in a data center, based on continuous streams of temperature and airflow measurements. Our approach is (a) capable of capturing cyberphysical interactions and automatically learning them from data; (b) computationally and physically scalable to data center scales; (c) able to provide online prediction with real-time sensor measurements. The papers main contributions are: (i) We provide a systematic approach to integrate physical laws and sensor observations in a data center; (ii) We provide an algorithm that uses sensor data to learn the parameters of a data centers cyber-physical system. In turn, this ability enables us to reduce model complexity compared to full-fledged fluid dynamics models, while maintaining forecast accuracy; (iii) Unlike previous simulation-based studies, we perform experiments in a production data center. Using real data traces, we show that ThermoCast forecasts temperature better than a machine learning approach solely driven by data, and can successfully predict thermal alarms 4.2 minutes ahead of time.

Typhoon: a reliable data dissemination protocol for wireless sensor networks

We present Typhoon, a protocol designed to reliably deliver large objects to all the nodes of a wireless sensor network (WSN). Typhoon uses a combination of spatially-tuned timers, prompt retransmissions, and frequency diversity to reduce contention and promote spatial re-use. We evaluate the performance benefits these techniques provide through extensive simulations and experiments in an indoor testbed. Our results show that Typhoon is able to reduce dissemination time and energy consumption by up to three times compared to Deluge. These improvements are most prominent in sparse and lossy networks that represent real-life WSN deployments.

Caiipa: automated large-scale mobile app testing through contextual fuzzing

Scalable and comprehensive testing of mobile apps is extremely challenging. Every test input needs to be run with a variety of contexts, such as: device heterogeneity, wireless network speeds, locations, and unpredictable sensor inputs. The range of values for each context, e.g. location, can be very large. In this paper we present Caiipa, a cloud service for testing apps over an expanded mobile context space in a scalable way. It incorporates key techniques to make app testing more tractable, including a context test space prioritizer to quickly discover failure scenarios for each app. We have implemented Caiipa on a cluster of VMs and real devices that can each emulate various combinations of contexts for tablet and phone apps. We evaluate Caiipa by testing 265 commercially available mobile apps based on a comprehensive library of real-world conditions. Our results show that Caiipa leads to improvements of 11.1x and 8.4x in the number of crashes and performance bugs discovered compared to conventional UI-based automation (i.e., monkey-testing).

Wireless sensor networks for soil science

Wireless sensor networks can revolutionise soil ecology by providing measurements at temporal and spatial granularities previously impossible. This paper presents our first steps towards fulfilling that goal by developing and deploying two experimental soil monitoring networks at urban forests in Baltimore, MD. The nodes of these networks periodically measure soil moisture and temperature and store the measurements in local memory. Raw measurements are incrementally retrieved by a sensor gateway and persistently stored in a database. The database also stores calibrated versions of the collected data. The measurement database is available to third-party applications through various Web Services interfaces. At a high level, the deployments were successful in exposing high-level variations of soil factors. However, we have encountered a number of challenging technical problems: need for low-level programming at multiple levels, calibration across space and time, and sensor faults. These problems must be addressed before sensor networks can fulfil their potential as high-quality instruments that can be deployed by scientists without major effort or cost.

SIFT: building an internet of safe things

As the number of connected devices explodes, the use scenarios of these devices and data have multiplied. Many of these scenarios, e.g., home automation, require tools beyond data visualizations, to express user intents and to ensure interactions do not cause undesired effects in the physical world. We present SIFT, a safety-centric programming platform for connected devices in IoT environments. First, to simplify programming, users express high-level intents in declarative IoT apps. The system then decides which sensor data and operations should be combined to satisfy the user requirements. Second, to ensure safety and compliance, the system verifies whether conflicts or policy violations can occur within or between apps. Through an office deployment, user studies, and trace analysis using a large-scale dataset from a commercial IoT app authoring platform, we demonstrate the power of SIFT and highlight how it leads to more robust and reliable IoT apps.