Abstract: Distributed video applications require packet delivery in real time. Compressed video exhibits high variability, so video smoothing techniques have been developed to reduce bit rate variability. This facilitates resource allocation to video streams. Scheduling the packets from various

The need for on-demand provisioning of wavelength routed channels with service differentiated offerings within the transport layer has become more essential due to the recent emergence of high bit rate IP network applications such as multimedia conferencing systems, video on demand systems, real-time

Network heterogeneity is a major issue and multimedia applications have to deal with interconnected networks consisting of many sub-networks of non-uniformly distributed resources. Real-time traffic caused by video sources is bursty by nature, resulting in buffer overflow at the switch

. Network heterogeneity is a major issue and multimedia applications have to deal with interconnected networks consisting of many sub-networks of non-uniformly distributed resources. Real-time traffic caused by video sources is bursty by nature, resulting in buffer overflow at the switch

these protocols build and maintain substantial network state to cope with the dynamics of low-power wireless links. Recent protocols depart from this design by leveraging synchronous transmissions (ST), whereby multiple nodes simultaneously transmit towards the same receiver, as opposed to pairwise link

Despite being a core networking primitive, collection protocols today often suffer from poor reliability (e.g., 70%) in practice, and heavily used protocols have never been evaluated in terms of communication efficiency. Using detailed experimental studies, we describe three challenges that cause existing collection protocols to have poor reliability and waste energy: inaccuracies in link estimation, link dynamics, and transient loops. In this paper we present Magnet, a robust, efficient, and hardware-independent collection protocol. Magnet uses three novel techniques to address these challenges. Magnet’s link estimator addresses the inaccuracies in link estimation by using feedback from both the data and control planes, using information from multiple layers through narrow, platform-independent interfaces. Second, Magnet addresses link dynamics by using the Trickle algorithm for control traffic, sending few beacons in stable topologies yet quickly adapting to changes. Finally, Magnet addresses transient loops by using data traffic as active topology probes, quickly discovering and fixing routing failures. Magnet runs on six different mote platforms and we have tested it on four testbeds. In most experiments, Magnet achieves 99 % reliability, and in some cases 99.9%. In the most challenging testbed, the state-of-the-art collection protocol today (MultiHopLQI) achieves 70 % reliability: Magnet achieves 97%. Magnet achieves this ten-fold reduction in dropped packets with 25 % fewer transmissions. Magnet works seamlessly on top of existing low-power MAC layers. Together, these results suggest that Magnet can be the robust, efficient collection layer that so many sensor network applications and protocols need. 1

in partial ful2llment of the requirements for the degree of

I would like to thank Prof. Dr. Torsten Braun, head of the Computer Network and Distributed Systems group (RVS), for supervising this work and for his insightful advises. Prof. Dr. Torsten Braun encouraged and motivated me to publish my research results and he provided me the opportunity to present the work on various conferences, for which I thank him. I would also like to thank Prof. Dr. Roger Wattenhofer, responsible for the Koreferat of this work. Also, Prof. Dr. Oscar Nierstrasz who was willing to be the co-examinator of this work deserves many thanks. Many thanks go to my colleagues of the RVS group and of the IAM for our various interesting discussions about all kinds of topics and for making the institute a very pleasant and friendly place to work at. Special thanks go to David Steiner, Marc Steinemann, Matthias Scheidegger, Florian Baumgartner, Ruy De Oliveira, and Attila Weyland. There are many students who worked with me and helped a lot in developing and implementing. Among them I especially thankful to Thomas Bernoulli,

We present Flush, a reliable, high goodput bulk data trans-port protocol for wireless sensor networks. Flush provides end-to-end reliability, reduces transfer time, and adapts to time-varying network conditions. It achieves these proper-ties using end-to-end acknowledgments, implicit snooping

Video streaming has become one of the most popular net-worked applications and, with the increased bandwidth and computation power of mobile devices, anywhere and anytime streaming has become a reality. Unfortunately, it remains a challenging task to compress high-quality video in real-time