Rate adaptation is a mechanism unspecified by the 802.11 standards, yet critical to the system performance by exploiting the multi-rate capability at the physical layer. In this paper, we conduct a systematic and experimental study on rate adaptation over 802.11 wireless networks. Our main contributions are two-fold. First, we critique five design guidelines adopted by most existing algorithms. Our study reveals that these seemingly correct guidelines can be misleading in practice, thus incur significant performance penalty in certain scenarios. The fundamental challenge is that rate adaptation must accurately estimate the channel condition despite the presence of various dynamics caused by fading, mobility and hidden terminals. Second, we design and implement a new Robust Rate Adaptation Algorithm (RRAA) that addresses the above challenge. RRAA uses short-term loss ratio to opportunistically guide its rate change decisions, and an adaptive RTS filter to prevent collision losses from triggering rate decrease. Our extensive experiments have shown that RRAA outperforms three well-known rate adaptation solutions (ARF, AARF, and SampleRate) in all tested scenarios, with throughput improvement up to 143%.

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Although data forwarding algorithms and protocols have been among the first set of issues explored in sensor networking, how to reliably deliver sensing data through a vast field of small, vulnerable sensors remains a research challenge. In this paper we present GRAdient Broadcast (GRAB), a new set of mechanisms and protocols which is designed specifically for robust data delivery in face of unreliable nodes and fallible wireless links. Similar to previous work [12,13], GRAB builds and maintains a cost field, providing each sensor the direction to forward sensing data. Different from all the previous approaches, however, GRAB forwards data along a band of interleaved mesh from each source to the receiver. GRAB controls the width of the band by the amount of credit carried in each data message, allowing the sender to adjust the robustness of data delivery. GRAB design harnesses the advantage of large scale and relies on the collective efforts of multiple nodes to deliver data, without dependency on any individual ones. We have evaluated the GRAB performance through both analysis and extensive simulation. Our analysis shows quantitatively the advantage of interleaved mesh over multiple parallel paths. Our simulation further confirms the analysis results and shows that GRAB can successfully deliver over 90% of packets with relatively low energy cost, even under the adverse conditions of 30% node failures compounded with 15% link message losses.

Sink mobility brings new challenges to data dissemination in large sensor networks. It suggests that information about each mobile sink's location be continuously propagated throughout the sensor field in order to keep all sensors informed of the direction of forwarding future data reports. Unfortunately, frequent location updates from multiple sinks can lead to both excessive drain of sensors' limited battery supply and increased collisions in wireless transmissions. In this paper, we describe TTDD, a Two-Tier Data Dissemination approach that provides scalable and efficient data delivery to multiple, mobile sinks. Each data source in TTDD proactively constructs a grid structure, which enables mobile sinks to continuously receive data on the move by flooding queries within a local cell only. TTDD's design exploits the fact that sensors are stationary and location-aware to construct and maintain the grid infrastructure with low overhead. We evaluate TTDD through both analysis and extensive simulations. Our results show that TTDD handles sink mobility effectively with performance comparable with that of stationary sinks.

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