PDF-Swarm Intelligence for Routing in Communication Networks I

Kassabalidis MAElSharkawi RJMarks II P Arabshahi AAGray Abstract Swarm intelligence as demonstrated by natural biological swarms has numerous powerful properties desirable in many engineering systems such as network routing In addition new paradig. e making routing decision It should process properties like correctness simplici ty robustness stability fairness and optimality Note optimality is always with re spect to chosen criterion Optimality principle if router is on optimal path from rout Ad-hoc . On-Demand Distance Vector . Routing. &. DSR. : The Dynamic Source Routing . Protocol for. . Multi-Hop . Wireless Ad Hoc Networks. November 2011,. Giorgos . Papadakis. & Manolis. EETS 8316 wireless networks. . SUBMITTED BY. . ONKAR BIWALKAR SMU ID : 39603152. Kazuya Sakai. , Min-. Te. Sun, Wei-Shinn Ku, . Jie. Wu, and Ten H Lai. Tokyo Metropolitan University. , National Central University, Auburn University, Temple University, The Ohio State University. Ran Manevich, Israel Cidon, Avinoam Kolodny, . Isask. ’. har. (Zigi) Walter and Shmuel Wimer. Technion. . –. Israel Institute of Technology. Group. Research. QNoC. Global traffic information is essential to make the right decision!. A proposal for an integrated new routing protocol using existing technologies used for underwater communications. By Prajit Kumar Das. For CMSC 601: Research Skills in Computer Science. Date: April 20. Reading Assignment. Sections . 4.1-4.5. Alternative: relevant papers. . References provided. Overview. Separate the routing decisions from implementation. Choice of path vs. . Performance. E.g., buffering, route computation implementation. Karthik. . Narayan. Primary Advisor : Dr. Geoffrey Fox. 1. Outline. 2. Objective. EST Sequence Assembly. The Problem. SWARM. Tools. Results. Future Work. Objective. Use the SWARM service and leverage the High Performance clusters for EST Sequence Assembly.. 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. Presenter : Chun-Ping Wu. 　. Authors :Jeffrey . Davcitz. , . Jiye. Yu, . Sugato. . Basu. , David . Gutelius. , Alexandra Harris. KDD 2007. 國立雲林科技大學. National Yunlin University of Science and Technology. Presented to the. Wasatch Beekeepers Association. b. y. George Denton. March 20, 2017. Ways. to Get . N. otified. About a Swarm. Get on the Swarm List here. at the WBA. Join. . Utah Swarm Removal. Nils Olsen and the Swarm SCARF Team. Swarm Data Overview. Magnetic Level 1b Data assessment. SCARF: Level 2 Data Products and Processing. Model of the Earth’s magnetic field including Swarm data. Patrick . “the swarm seems more intelligent than the individual in it”; insects: “the swarm exhibits behavior that individual members cannot”. Stigmergy. : indirect communication (using signs or clues placed in the environment). Redes. y . Servicios. . Ubicuos. y . Seguros. Unit 4: Network Technologies. Ana Belén García Hernando. abgarcia@diatel.upm.es. , . anabelen.garcia@upm.es. Networking challenges in WSN. Low energy consumption [vs. .