PPT-TCP over Constrained-Node Networks

draftgomezlwigtcpconstrained nodenetworks03 Carles Gomez Universitat Politècnica de Catalunya Jon Crowcroft University of Cambridge Michael Scharf Nokia IETF 99 Prague July 2017. EETS 8316 wireless networks. . SUBMITTED BY. . ONKAR BIWALKAR SMU ID : 39603152. Shantanu. . Dutt. Univ. of Illinois at Chicago. Acknowledgement. Adapted from Chapter 2 slides of the text, by A. . Grama. w/ a few changes and augmentations. Interconnection Networks . for Parallel Computers . Jure . Leskovec. , CMU. Lars . Backstrom. , Cornell. Ravi Kumar, Yahoo! Research. Andrew Tomkins, Yahoo! Research. Introduction. Social networks . evolve. with additions and deletions of nodes and edges. in Ad Hoc Networks using Game Theory. March . 25, 2010. EE5723 – Computer & . Network Security. Presentation Outline . Big Picture Topic Introduction. Game Theory Brief Overview. Applications in Ad Hoc Networks. Advisor. :. H. E. Stanley. Collaborators:. B. Podobnik. S. Havlin. S. V. Buldyrev. D. Kenett. Antonio Majdandzic. Boston University. 1. Motivation. 2. Outline. Model. Numerical results and theory. Presented By. Al-Sakib Khan Pathan. Department of Computer Science. International Islamic University Malaysia. On the Key Revocation Schemes in Wireless Sensor Networks. Dieynaba. Mall. 1. , . Karim. inWireless. Sensor Networks. Abstract. Wireless sensor networks are vulnerable to the node clone, and several distributed protocols have been proposed to . de¬tect. this attack. . However, they require too strong assumptions to be practical for large-scale, randomly deployed sensor networks. . Sushmita Roy. sroy@biostat.wisc.edu. Computational Network Biology. Biostatistics & Medical Informatics 826. Computer Sciences 838. https://. compnetbiocourse.discovery.wisc.edu. Sep 27. th. 2016. Konstantin Zuev. http. ://www.its.caltech.edu/~zuev. /. University of Southern California. Probability and Statistics Seminar . Sept 9, 2016. Background: Complex Networks. What are networks?. The Oxford English Dictionary: . Indirect Networks. Cost Model. Comparison of Direct and Indirect Networks. Classification. Shared medium networks. Example: backplane buses. Direct networks. Example: . k. -ary . n. -cubes, meshes, and trees. Map nodes to low-dimensional . embeddings. .. 2) Graph neural networks. Deep learning architectures for graph-structured data. 3) Applications. Representation Learning on Networks, snap.stanford.edu/proj/embeddings-www, WWW 2018. ). Prof. . Ralucca Gera, . Applied Mathematics Dept.. Naval Postgraduate School. Monterey, California. rgera@nps.edu. Excellence Through Knowledge. Learning Outcomes. I. dentify . network models and explain their structures. Here in mesh network nodes are arranged as a q-dimensional lattice. The neighboring nodes are only allowed to communicate the data in one step i.e., each . PEi. is allowed to send the data to any one of PE(i+1) , PE (i-1), . Machine Learning Department. Carnegie Mellon University. Cyberspace. The emergence of ‘cyberspace’ and the World Wide Web is like the discovery of a new continent.. Jim Gray, 1998 Turing Award address.