TCP is a reliable transport protocol tuned to perform well in traditional networks made up of links with low bit-error rates. Networks with higher bit-error rates, such as those with wireless links and mobile hosts, violate many of the assumptions made by TCP, causing degraded end-to-end performance. In tbis paper, we describe the design and implementation of a simple protocol, called the snoop protocol, that improves TCP performance in wireless networks. The protocol modifies network-layer software mainly at a base station and preserves end-to-end TCP semantics. The main idea of the protocol is to cache packets at the base station and perform local retransmissions across the wireless link. We have implemented the snoop protocol on a wireless testbed consisting of IBM ThinkPad laptops and i486 base stations communicating over an AT&T Wavelan. Our experiments show that it is significantly more robust at dealing with unreliable wireless links as compared to normal TCP; we have achieved throughput speedups of up to 20 times over regular TCP in our experiments with the protocol. Intrcdu.ctionRecent activity in mobile computing and wireless networks strongly indicates that mobile computers and their wireless communiication links will be an integral part of future internetworks.. Communication over wireless links is characterized by limited bandwidth, high latencies, high bit-error rates and temporary disconnections that must be dealt with by network protocols and applications. In addition, protocols and applications have to handle user mobility and the handoffs that occur as users move from cell to cell in cellular wireless networks. These handoffs involve transfer of communication state (typically network-level state) from 1. 2 one base station (a router between a wired and wireless network) to another, and typically last anywhere between a few tens to a few hundreds of milliseconds.Reliable transport protocols such as TCP [Pos81, Ste94, Bra891 have been tuned for traditional networks made up of wired links ant3 stationary hosts. TCP performs very well on such networks by adapting to end-to-end delays and packet losses caused by congestion. TCP provides reliability by maintaining a running average of estimated round-trip delay and mean deviation, and by retransmitting any packet whose acknowledgment is not received within four times the deviation from the average. Due to the relatively low bit-error rates over wired networks, all packet losses are correctly assumed to be because of congestion.In the presence of the high error rates and intermittent connectivity characteristic of wireless links, TCP reacts to packet losses as it would in the wired environment: it drops its transmission window size before retransmitting packets, initiates congestion control or avoidance mechanisms (e.g., slow start [Jac88]) and resets its retransmission timer (Karn's Algorithm [KP87]). These measures result in an unnecessary reduction in the link's bandwidth utilizatior:, thereby causing a significant degradation in performance in the ...

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1] With large volumes of data arriving in near real time from environmental sensors, there is a need for automated detection of anomalous data caused by sensor or transmission errors or by infrequent system behaviors. This study develops and evaluates three automated anomaly detection methods using dynamic Bayesian networks (DBNs), which perform fast, incremental evaluation of data as they become available, scale to large quantities of data, and require no a priori information regarding process variables or types of anomalies that may be encountered. This study investigates these methods' abilities to identify anomalies in eight meteorological data streams from Corpus Christi, Texas. The results indicate that DBN-based detectors, using either robust Kalman filtering or Rao-Blackwellized particle filtering, outperform a DBN-based detector using Kalman filtering, with the former having false positive/negative rates of less than 2%. These methods were successful at identifying data anomalies caused by two real events: a sensor failure and a large storm.

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