Surge: A Network Analysis Tool Crossbow Technology.

Slides:



Advertisements
Similar presentations
Mobile Ad-hoc Network Simulator: mobile AntNet R. Hekmat * (CACTUS TermiNet - TU Delft/EWI/NAS) and Radovan Milosevic (MSc student) Mobile Ad-hoc networks.
Advertisements

A 2 -MAC: An Adaptive, Anycast MAC Protocol for Wireless Sensor Networks Hwee-Xian TAN and Mun Choon CHAN Department of Computer Science, School of Computing.
Transmission Power Control in Wireless Sensor Networks CS577 Project by Andrew Keating 1.
Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad Hoc Networks By C. K. Toh.
TDMA Scheduling in Wireless Sensor Networks
pTunes: Runtime Parameter Adaptation for Low-power MAC Protocols
Kyung Tae Kim, Hee Yong Youn (Sungkyunkwan University)
TOPOLOGIES FOR POWER EFFICIENT WIRELESS SENSOR NETWORKS ---KRISHNA JETTI.
S-MAC Sensor Medium Access Control Protocol An Energy Efficient MAC protocol for Wireless Sensor Networks.
Introduction to Sensor Networks Rabie A. Ramadan, PhD Cairo University 4.
Monday, June 01, 2015 ARRIVE: Algorithm for Robust Routing in Volatile Environments 1 NEST Retreat, Lake Tahoe, June
Background In designing communication protocols for use in wireless sensor networks, one must consider the limitations of wireless systems in general:
1 Prediction-based Strategies for Energy Saving in Object Tracking Sensor Networks Yingqi Xu, Wang-Chien Lee Proceedings of the 2004 IEEE International.
Mobile and Wireless Computing Institute for Computer Science, University of Freiburg Western Australian Interactive Virtual Environments Centre (IVEC)
KUASAR An efficient and light-weight protocol for routing and data dissemination in ad hoc wireless sensor networks David Andrews Aditya Mandapaka Joe.
1 Relates to Lab 4. This module covers link state routing and the Open Shortest Path First (OSPF) routing protocol. Dynamic Routing Protocols II OSPF.
1 Ultra-Low Duty Cycle MAC with Scheduled Channel Polling Wei Ye Fabio Silva John Heidemann Presented by: Ronak Bhuta Date: 4 th December 2007.
Taming the Underlying Challenges of Reliable Multihop Routing in Sensor Networks.
LPT for Data Aggregation in Wireless Sensor networks Marc Lee and Vincent W.S Wong Department of Electrical and Computer Engineering, University of British.
Matnet – Matlab Network Simulator for TinyOS Alec WooTerence Tong July 31 st, 2002.
Power Scheduling at the Network Layer for wireless sensor networks Barbara Hohlt Eric Brewer UC Berkeley NEST Retreat June 2004.
A Transmission Control Scheme for Media Access in Sensor Networks Presented by Jianhua Shao.
Adaptive Self-Configuring Sensor Network Topologies ns-2 simulation & performance analysis Zhenghua Fu Ben Greenstein Petros Zerfos.
A Transmission Control Scheme for Media Access in Sensor Networks Alec Woo, David Culler (University of California, Berkeley) Special thanks to Wei Ye.
Wei Hong January 16, 2003 Overview of the Generic Sensor Kit (GSK)
Empirical Analysis of Transmission Power Control Algorithms for Wireless Sensor Networks CENTS Retreat – May 26, 2005 Jaein Jeong (1), David Culler (1),
Reliable, Robust Data Collection in Sensor Networks Murali Rangan Russell Sears Fall 2005 – Sensornet.
1 Topology Control of Multihop Wireless Networks Using Transmit Power Adjustment Infocom /12/20.
Modeling & Simulation of Bluetooth MAC protocol COE543 Term Project Spring 2003 Submitted by: H.M.Asif (ID# )
CS2510 Fault Tolerance and Privacy in Wireless Sensor Networks partially based on presentation by Sameh Gobriel.
Itrat Rasool Quadri ST ID COE-543 Wireless and Mobile Networks
Hamida SEBA - ICPS06 June 26 th -29 th Lyon France 1 ARMP: an Adaptive Routing Protocol for MANETs Hamida SEBA PRISMa Lab. – G2Ap team
1 CS 4396 Computer Networks Lab Dynamic Routing Protocols - II OSPF.
A Transmission Control Scheme for Media Access in Sensor Networks Alec Woo and David Culler University of California at Berkeley Intel Research ACM SIGMOBILE.
Project Introduction 이 상 신 Korea Electronics Technology Institute.
2008/2/191 Customizing a Geographical Routing Protocol for Wireless Sensor Networks Proceedings of the th International Conference on Information.
Weaponizing Wireless Networks: An Attack Tool for Launching Attacks against Sensor Networks Thanassis Giannetsos Tassos Dimitriou Neeli R. Prasad.
Ubiquitous Networks WSN Routing Protocols Lynn Choi Korea University.
Switching breaks up large collision domains into smaller ones Collision domain is a network segment with two or more devices sharing the same Introduction.
1 A Distributed Architecture for Multimedia in Dynamic Wireless Networks By UCLA C.R. Lin and M. Gerla IEEE GLOBECOM'95.
/42 Does Wireless Sensor Network Scale? A Measure Study on GreenOrbs Yunhao Liu, Yuan He, Mo Li, Jiliang Wang,Kebin Liu, Lufeng Mo, Wei Dong,
Secure Routing in Wireless Sensor Networks: Attacks and Countermeasures Chris Karlof and David Wagner (modified by Sarjana Singh)
College of Engineering Grid-based Coordinated Routing in Wireless Sensor Networks Uttara Sawant Major Advisor : Dr. Robert Akl Department of Computer Science.
REED: Robust, Efficient Filtering and Event Detection in Sensor Networks Daniel Abadi, Samuel Madden, Wolfgang Lindner MIT United States VLDB 2005.
1 REED: Robust, Efficient Filtering and Event Detection in Sensor Networks Daniel Abadi, Samuel Madden, Wolfgang Lindner MIT United States VLDB 2005.
Simulation of the OLSRv2 Protocol First Report Presentation.
Energy and Latency Control in Low Duty Cycle MAC Protocols Yuan Li, Wei Ye, John Heidemann Information Sciences Institute, University of Southern California.
Data Collection and Dissemination. Learning Objectives Understand Trickle – an data dissemination protocol for WSNs Understand data collection protocols.
KAIS T High-throughput multicast routing metrics in wireless mesh networks Sabyasachi Roy, Dimitrios Koutsonikolas, Saumitra Das, and Y. Charlie Hu ICDCS.
A Power Assignment Method for Multi-Sink WSN with Outage Probability Constraints Marcelo E. Pellenz*, Edgard Jamhour*, Manoel C. Penna*, Richard D. Souza.
Multi-channel Wireless Sensor Network MAC protocol based on dynamic route.
Low Power, Low Delay: Opportunistic Routing meets Duty Cycling Olaf Landsiedel 1, Euhanna Ghadimi 2, Simon Duquennoy 3, Mikael Johansson 2 1 Chalmers University.
Modeling In-Network Processing and Aggregation in Sensor Networks Ajay Mahimkar The University of Texas at Austin March 24, 2004.
We hope that it is more important to know where you are going than to get there quickly. SNU INC Lab. A Survey of Energy Efficient Network Protocols for.
A Dynamic Query-tree Energy Balancing Protocol for Sensor Networks H. Yang, F. Ye, and B. Sikdar Department of Electrical, Computer and systems Engineering.
Self-stabilizing energy-efficient multicast for MANETs.
UNIT IV INFRASTRUCTURE ESTABLISHMENT. INTRODUCTION When a sensor network is first activated, various tasks must be performed to establish the necessary.
RBP: Robust Broadcast Propagation in Wireless Networks Fred Stann, John Heidemann, Rajesh Shroff, Muhammad Zaki Murtaza USC/ISI In SenSys 2006.
Thermal Detecting Wireless Sensor Network Presenters: Joseph Roberson, Gautam Ankala, and Jessica Curry Faculty Advisor: Dr. Linda Milor ECE 4007: Final.
On Mobile Sink Node for Target Tracking in Wireless Sensor Networks Thanh Hai Trinh and Hee Yong Youn Pervasive Computing and Communications Workshops(PerComW'07)
Oregon Graduate Institute1 Sensor and energy-efficient networking CSE 525: Advanced Networking Computer Science and Engineering Department Winter 2004.
MAC Protocols for Sensor Networks
IHP: Innovation for High Performance Microelectronics
Simulators for Sensor Networks
IoT Network Monitor.
Energy-Efficient Communication Protocol for Wireless Microsensor Networks by Wendi Rabiner Heinzelman, Anantha Chandrakasan, and Hari Balakrishnan Presented.
Wireless Sensor Networks
Ultra-Low Duty Cycle MAC with Scheduled Channel Polling
REED : Robust, Efficient Filtering and Event Detection
Presentation transcript:

Surge: A Network Analysis Tool Crossbow Technology

Outline Discussion of 3 improvements to Surge tool Overview of routing protocol performance as captured by Surge Discussion of low-power routing performance

Updated Surge GUI Quality = Link Yield to Parent Yield = % of Data Packets Received Prediction = Product of Quality metrics on all links to base

Log File History View Allows one to scroll through log files Shows data yield across time Click on time-line to show network topology at that time Reveals flaws in routing algorithms/performance

History Viewer Statistics Reports overall analysis of collected data. Example output of Stats program showing reliable, stable networking.

Reliable Mint-Route Routing Algorithm Attempts to optimize expected success rate (Prediction in GUI) Each node monitors up to 16 neighbors Each node reports its receive link quality from each neighbor Cost metric used to represent success rate to base station Each node broadcasts its cost Node cost is parent cost plus link cost to parent Nodes try to minimize total cost Data packets are acknowledged by parents and retransmitted up to 5 times

Mint-Route Results Avoids low-quality links seen in TinyOS 1.1 release version Prevents nodes from incorrectly attracting children In test network, it improved worst-case yield from 63% to 99%. Acknowledgements cost 6 ms of extra activity per message (less than typical MAC delay)

Routing Improvement Results

Power Reduction Goal: 1 year lifetime, 100 nodes, 1 base, 3 minutes/report Methodology: Analyze current performance Build analytical model to explain current performance Modify algorithm to meet goal using analytical model Confirm on real system

Analysis Model Includes: Network reconfiguration operations Data originating from node Data routed by node (ack/no_ack) Data being transmitted in communication range of node Estimate for worst-case node in 100 node network: Route data for 20 nodes, hear over 200 data packets. (no base optimization)

Analytical Analysis of Activity by Node

11 Months Energy Breakdown 13 Months 10 Months

Confirming Performance Can’t wait a year to see if it works… Internal power meter allows one to track power consumption Record milliamp-minute use by radio, CPU and if necessary sensors Report total power usage via multi-hop network

Power Visualization Nodes 2, 4 and 7 are “white hot” because they hear and route a lot of traffic. Nodes 9, 12 and 13 are cool because they are leaf nodes at the edge of the network. Analysis must focus on the “hot” nodes. Analytical models predict this network to last 1.5 years reporting every 3 minutes. The base rarely transmits and is the coolest.

Final Network Statistics