7749218
Impact of channel conditions on the throughput optimization in 802.11 DCF
In this paper, we evaluate the impact of transmission error rate on the contention and the system throughput in IEEE 802.11 DCF. We identify the trade-off between the desire to reduce the ratio of overhead in the data packet by adopting larger packet size and the need to reduce the packet error rate by using smaller packet length. Meanwhile, we provide a novel approach to model the different channel conditions incurred by different nodes. This work has applications in performance optimization by establishing trade-offs between transmission error rate and transmission power, modulation, coding scheme, packet size. Our results substantiate the intuition that in a contention-based system like 802.11 DCF, the channel variation of any single node in the system not only impacts the throughput of itself, but also affects the throughput of other nodes and the system throughput. Our work shows that to achieve maximum optimization of the throughput, the channel conditions of all the nodes in the system have to be taken into account. Otherwise the optimization is suboptimal. Copyright © 2005 John Wiley & Sons, Ltd.
43330192
Impact of bursty error rates on the performance of wireless local area network (WLAN)
Abstract With an increasing popularity of DCF based wireless LAN, the modeling of 802.11 distributed coordination function (DCF) has attracted lots of research attention. Existing analysis of 802.11 DCF has been focused on the determination of the throughput and the packet delay under saturated traffic and ideal channel conditions. Although some recent papers address the saturated performance under a simple uniform error model, they can hardly capture the impact of bursty characteristics of wireless fading on the performance of 802.11 DCF. This paper presents exact formulae for the throughput and the delay in DCF for various traffic conditions when either saturated or unsaturated traffic load is present. A two-state Markov channel model is incorporated to present the bursty characteristics of channel errors. With our analysis, the impact of bursty channel error on unsuccessful transmission probability and the DCF performance can be determined. The results of our analytical framework reveal that the four-way handshaking scheme does not improve throughput substantially for light traffic load. However, for heavy traffic load, the four-way handshaking scheme is advantageous as compared to the basic access scheme. Also, extensive simulation is done to substantiate the accuracy of our analytical model.
2664026
Simulating the Growth of Viruses
To explore how the genome of an organism defines its growth, we have developed a computer simulation for the intracellular growth of phage T7 on its E. coli host. Our simulation, which incorporates 30 years of genetic, biochemical, physiological, and biophysical data, is used here to study how the intracellular resources of the host, determined by the specific growth rate of the host, contribute toward phage development. It is also used to probe how changes in the linear organization of genetic elements on the T7 genome can affect T7 development. Further, we show how time-series trajectories of T7 mRNA and protein levels generated by the simulation may be used as raw data to test data-mining strategies, specifically, to identify partners in protein-protein interactions. Finally, we suggest how generalization of this work can lead to a knowledge-driven simulation for the growth of any virus.
456221
Integrating hidden Markov models and spectral analysis for sensory time series clustering
We present a novel approach for clustering sequences of multi-dimensional trajectory data obtained from a sensor network. The sensory time-series data present new challenges to data mining, including uneven sequence lengths, multi-dimensionality and high levels of noise. We adopt a principled approach, by first transforming all the data into an equal-length vector form while keeping as much temporal information as we can, and then applying dimensionality and noise reduction techniques such as spectral clustering to the transformed data. Experimental evaluation on synthetic and real data shows that our proposed approach outperforms standard model-based clustering algorithms for time series data.
16826835
High-Level Goal Recognition in a Wireless LAN
Plan recognition has traditionally been developed for logically encoded application domains with a focus on logical reasoning. In this paper, we present an integrated plan-recognition model that combines low-level sensory readings with high-level goal inference. A two-level architecture is proposed to infer a user's goals in a complex indoor environment using an RF-based wireless network. The novelty of our work derives from our ability to infer a user's goals from sequences of signal trajectory, and the ability for us to make a trade-off between model accuracy and inference efficiency. The model relies on a dynamic Bayesian network to infer a user's actions from raw signals, and an N-gram model to infer the users' goals from actions. We present a method for constructing the model from the past data and demonstrate the effectiveness of our proposed solution through empirical studies using some real data that we have collected.