High-quality wireless link models can enable better simulations and reduce the development time for new algorithms and protocols. However, the models underlying current simulators are either based on too simple assumptions, so they are unrealistic, or are based on sophisticated machine learning techniques that require extensive training data from the target link, so they are more realistic but impractical. We consider the practical scenario where data collection time is limited (e.g. a few minutes) and cannot afford to deploy a testbed infrastructure with cabling, power and storage. We propose techniques that can construct an accurate machine learning model of the short-term behavior of a target wireless link given only limited training data for the latter, by adapting a reference model that was trained with abundant data. The parameters of the target model are a constrained transformation of the parameters of the reference model, thus the actual number of free parameters is much smaller, and can be reliably estimated with much less data. While estimating the target model from scratch requires 1 to 5 hours of target link data, we show our adaptation technique only requires under 3 minutes of data, for all packet reception rate regimes. We also show that we can construct adapted models for target links in different environments, packet sizes, interference conditions and radio technology (802.15.4 or 802.11b).

Modeling the behavior of 802.15.4 links is a nontrivial problem, because 802.15.4 links experience different level of dynamics at short and long time scales. This makes the design of a suitable model that combines the different dynamics at different time scales a nontrivial problem. We propose a novel multilevel approach, the M&M model, involving hidden Markov models (HMMs) and mixtures of multivariate Bernoullis (MMBs) for modeling the long and short time-scale behavior of wireless links from 802.15.4 test beds. We characterize the synthetic traces generated from our model of the wireless link in terms of the mean and variance of the packet reception rates from the data traces, comparison of distributions of run lengths, and conditional packet delivery functions of successive packet receptions (1's) and losses (0's). Our results show that when compared to the closest-fit pattern matching model in TOSSIM, the proposed modeling approach is able to mimic the behavior of the data traces quite closely, with differences in packet reception rates of the empirical and simulated traces of less than 1.9%; on average and 6.6% in the worst case. Moreover, the simulated links from our proposed approach were able to account for long runs of 1's and 0's as observed in empirical data traces.

The lossy nature of wireless communication leads to many challenges while designing multihop networks. As an integral part of reliable communication in wireless networks, effective link estimation is essential for routing protocols. Recent studies have shown that link reliability-based metrics like ETX have better performance than traditional metrics such as hop count or latency. Usually, such metrics employ techniques like blacklisting, involving thresholds during the link estimation process. In this paper, we conduct a detailed performance analysis of three commonly used link-quality metrics in wireless sensor networks: ETX, 4Bit, and RNP. We study the interplay between these metrics and CTP, a tree-based routing protocol provided by TinyOS. The objectives of our experiment are two fold. First, by applying different link-quality metrics to the same routing protocol, we provide extensive evaluation on ETX, 4Bit and RNP with insights on their performance under different criteria. Second, we study the impact of the presence or absence of a blacklisting policy when using these link quality estimation metrics. As to our knowledge, this paper is the first to compare the performance between these link quality based metrics with networks of different qualities under realistic conditions.

Running efficiency is an important factor to consider in order to avoid injury. In particular, foot pronation, the angle of the foot as it hits the ground, is a common cause for many types of injuries among runners. Though pronation is common, diagnosing pronation is difficult and imprecise. Currently there is no method of diagonsis which can quantify the severity of pronation. In this paper we propose using WISP (Wireless Identification and Sensing Platform) sensors to help identify and quantify foot pronation.

The ease of deploying wireless camera sensor nodes has grown with the reduction of manufacturing costs of low power, high resolution cameras. Although current wireless sensor network platforms have limited on-board resources for solving highly complex computer vision problems, we show that by splitting the processing costs between the sensor node and a powerful backend, we can achieve better classification results. Using such a distributed processing approach, we balance the computational and communication costs for achieving better detection performance while improving the system lifetime.

802.15.4 links experience different level of dynamics at short and long time scales. This makes the design of a suitable model that combines the different dynamics at different timescales a non-trivial problem. In this paper, we propose a novel multilevel approach involving Hidden Markov Models (HMMs) and Mixtures of Multivariate Bernoullis (MMBs) for modeling the long and short time scale behavior of wireless links using experimental data traces collected from multiple 802.15.4 testbeds. We characterize the synthetic traces generated from the model of the wireless link in terms of statistical characteristics as compared to an empirical trace with similar PRR characteristics, such as the mean and variance of the packet reception rates from the data traces, comparison of distributions of run lengths and conditional packet delivery functions of successive packet receptions (1's) and losses (0's). We modified TOSSIM to utilize data traces created using our modeling approach and compare them against the existing radio model in TOSSIM, which uses the Closest-fit Pattern Matching model for modeling variations in noise which affect the link quality. The results show that our proposed modeling approach is able to mimic the behavior of the data traces quite closely, with difference in packet reception rates of the empirical and simulated traces of less than 2.5% on average and 9% in the worst case. Moreover, the simulated links from our proposed approach were able to account for long runs of 1's and 0's as observed in empirical data traces.

Modeling the behavior of 802.15.4 links is a non-trivial problem because of the widespread heterogeneity in the quality of any given link over short and long time scales. We propose a novel multilevel approach involving Hidden Markov Models (HMMs) and Mixtures of Multivariate Bernoullis (MMBs) for modeling the long and short time scale behavior of wireless links using experimental data traces collected from multiple 802.15.4 testbeds. We characterize the synthetic traces generated from the proposed model in terms of statistical characteristics as compared to an empirical trace with similar PRR characteristics.

Heterogeneity has been considered in scheduling, but without taking into account the temporal variation of completion times of the sub-tasks for a divisible, independent task. In this paper, the problem of scheduling multiple, divisible independent tasks on a heterogeneous distributed computing system are addressed. The "stochastic" approach, which was previously applied to DAG scheduling, is employed for scheduling a group of multiple divisible as well as whole independent tasks. It explicitly considers the standard deviations (temporal heterogeneity) in addition to the mean execution times in deriving a schedule, in order to model more closely what would actually happen "on average" on a temporally heterogeneous system (instead of approximating the random weights by their means only as in other approaches). Through an extensive computer simulation, it has been shown that the proposed approach can improve schedules significantly over those by a scheme which uses the average weights only.