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Application Case-Studies in Mobile Sensor Networks

Application Case-Studies in Mobile Sensor Networks

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Application Case-Studies in Mobile Sensor Networks

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  1. Application Case-Studies in Mobile Sensor Networks Parixit Aghera Pejman Kalkhoran

  2. Agenda • What is a Sensor Network? • Why Mobile Sensor Networks? • Benefits of Mobile Sensor Networks • Example Applications • Mobile Sensor Nodes: ZebraNet, SWIM, • Mobile Robot: PlantCare • Observations

  3. What is a Sensor Network? • Micro-sensors, on-board processing, wireless interfaces feasible at very small scale--can monitor phenomena “up close” • Enables spatially and temporally dense environmental monitoring • Networked Sensing will reveal previously unobservable phenomena Definition from CENS presentation

  4. Why Not Traditional Solutions? • Long range communication  more power • Problem in Precise Deployment  Applications require ad-hoc deployment • Significant Human Intervention • Not scalable  Thousands of sensor nodes

  5. Mobile Sensor Networks • Various elements in sensor network can be mobile • Base Station – Better network coverage/power saving • Mobile Robot/s – Deployment/Maintenance/Data Collection • Sensor nodes – Because of underlying phenomena • Motility • Virtual Mobility • Software agents moving across the sensor nodes

  6. Benefits of Mobile Sensor Networks • Reduces number of sensors  scalability • Self-sustaining • Power Management • Recalibration • Improved data collection due to minimization of sensor uncertainty • Increased Power-Bandwidth Efficiency

  7. Energy Efficient Computing for Wildlife Tracking: Design Tradeoffs and Early Experiences with ZebraNet Philo Juang, Hidekazu Oki, Yong Wang, Margaret Martonosi, LiShiuan Peh, and Daniel Rubenstein Princeton University

  8. ZebraNet – Problem Definition • Gathering data and observations on a wide range of species • Understand species interactions and influences on each other • System must run autonomously for months • Large spatial scale (Thousands of km2) • Mobile nodes and mobile base station

  9. Zebra Mobility Model • Mobility models determine: • Speed, Direction, Frequency of movement, Forces of attraction

  10. Collar Design

  11. Collar Design • 12-Channel GPS Receiver with 32-bit microprocessor and 640 KB of user-available memory • Linx Short-Range Radio with 100m range and low power consumption • Slow, High-Powered Data Radio and Packet Modem with 8 km range

  12. Collar Assumptions • 30 position samples per hour, all day • 6 hours per day of searching for peer nodes and transferring data between them using short-range radio • 3 hours per day of searching for mobile base station • 640 Kb transferred to base in 5 day period

  13. Protocol Requirements • All data gathered must reach base station • Not all nodes have access to base station • Data must “hop” to the base station from node to node • Base station is not always active

  14. Protocol Design - Flooding • Flooding Protocol • Flood data to all neighbors upon discovery • Large bandwidth, energy, storage requirements

  15. Protocol Design – History-based • History-Based Protocol • Sends data based on prior communication patterns • Hierarchy determined by contact with base station

  16. Storage Maintenance • Node prioritizes own data over others • Data points with most recent timestamp have priority • Delete lists – data points transferred to base • Transferred between nodes • If data points are within delete list, they are removed from memory

  17. Results

  18. Results

  19. Possible Improvements • Calibration of measurement sensors • Node failure analysis • Storage parameters not specified • Lack of base station initiated communication • No strong reason for selected communication pattern

  20. Impala: A Middleware System for Managing Autonomic, Parallel Sensor Systems By Ting Liu and Margaret Martonosi Princeton University

  21. Impala • Middleware Architecture of Collar nodes • Major Contributions • Modular application framework • Event based application programming model • Non-VM based middleware for infrequent code updates • Application adaptivity mechanism • Implementation on iPAQ 206MHz, Linux

  22. Impala – System Architecture • Applications are composed of multiple modules • Runs one application at a time • Applications share global data structure

  23. Impala Application Adaptation • Maintains application parameters and system parameters • Switches between different application based on shared parameters and rules. • Takes into consideration device failure • Doesn’t switch to an application if a device used by application has failed

  24. Impala Application Update • Uses module based version system • Three stage protocols • Send software version info • Send module request if module version is old • Send module to requesting node

  25. The Shared Wireless Infostation Model - A New Ad Hoc Networking Paradigm(or Where there is a Whale, there is a Way) By Tara Small and Zygmunt J. Haas Cornell University

  26. SWIM • Proposes SWIM as combination of Infostation and ad-hoc network model for increased capacity and delay • Infostation - Geographically intermittent coverage at high speed. • Designs a biological information acquisition system for whales. • Several SWIM stations floating on water surface • Each whale is attached with a sensor node (tag) • Communication takes place only when whale is on water surface • Data collected by tag should be offloaded to any SWIM stations

  27. SWIM • Network Model • Information exchanged between tags with some probability • Information is offloaded to SWIM station • Each packet carried by a whale has time-to-live associated with it. • Packets are erased from memory when time-to-live expires • Mobility Model is based on • Feeding areas • Grouping behavior

  28. SWIM • Analytical Model • Models inter tag packet transfer as discrete Markov chain. Uses analogy of infectious disease • Defines relationship between time-to-live (T) and CDF F(T) of a packet being offloaded at one of the SWIM station • Defines relationship between various parameters and storage requirements of each tag • Parameters • β = contact rate γ = whale-station contact rate, S(t) = susceptible whale, R(t) = recovered whale, I(t) = infected whale, N = number of whales

  29. Making Sensor Networks Practical with Robots Anthony LaMarca, Waylon Brunette, David Koizumi, Matthew Lease, Stefan B. Sigurdsson, Kevin Sikorski, Dieter Fox, and Gaetano Borriello Intel Research Laboratory @ Seattle University of Washington

  30. PlantCare • PlantCare project objective: to build a zero-configuration and distraction-free system for automatic care of houseplants • Contributions of this paper: • Analysis of use of mobile robot in sensor networks • Demonstration of in-situ calibration • Implementation using pioneer robot and Berkeley motes

  31. Mobile Robot Use in sensor network • Context aware deployment of sensors • Robot can take sensor measurement at different places in environment and determine best place to deploy sensor. E.g. Temperature sensor • Continuous Calibration of Sensors • In-situ calibration • Recalibration at certain time interval • Recharging • A mobile robot goes to deployed sensor nodes and recharges them

  32. Hardware Implementation • Sensor Node • Berkeley Motes • Sensor – Photo resistor, thermistor, irrometer, charge monitor • Power source – Capacitor that can be recharged by inductive coil

  33. Hardware Implementation • Robot Hardware • Pioneer 2-DX mobile robot • Following custom hardware components controlled and monitored by a micro-controller • Small water tank with dispensing spout & pump • Inductive charging coil for sensor node • Inductive charge coil for robot • Human calibrated sensor node • Laptop with 802.11b connectivity • Maintenance Bay for charging and water refilling

  34. Software Implementation • Rain software infrastructure provides a framework in which applications are implemented as co-operating services that communicates via asynchronous events • PlantCare application is composed of 15 services running on laptop • Sensor Software • TinyOS – An event-triggered operating system

  35. Navigation, Deployment and Maintenances[6] Navigation Field computed by sensor node. Sensor Node Deployment Pictures and Animation from Maxim Batalin [6]

  36. Multi-Robot Task Allocation [7] Pictures and Animation from Maxim Batalin [6]

  37. Observations • Requires good understanding of problem domain • Problems are interdisciplinary in nature • For mobile sensors nodes, a good mobility model is required • Definition of distributed protocol for information exchange • Sensor Nodes and Mobile Robots can benefit from each other

  38. Summary • Introduced sensor networks and their characteristics • Introduced mobile sensor networks and their characteristics • Presented several examples of mobile sensor network applications • Mobile Sensor Nodes: ZebraNet, SWIM, • Mobile Robot: PlantCare • Presented our observations from these applications

  39. References • “Energy Efficient Computing for Wildlife Tracking: Design Tradeoffs and Early Experiences with ZebraNet”, Philo Juang, Hidekazu Oki, Yong Wang, Margaret Martonosi, LiShiuan Peh, and Daniel Rubenstein ASPLOS-X conference, October 2002 • “Impala: A Middleware System for Managing Autonomic, Parallel Sensor Systems”, Ting Liu and Margaret Martonosi PPoPP2003 • “The Shared Wireless Infostation Model - A New Ad Hoc Networking Paradigm (or Where there is a Whale, there is a Way)”, Tara Small and Zygmunt J. Haas, MobiHoc 2003 • “Making Sensor Networks Practical with Robots” Anthony LaMarca, Waylon Brunette, David Koizumi, Matthew Lease, Stefan B. Sigurdsson, Kevin Sikorski, Dieter Fox, and Gaetano Borriello, International Conference on Pervasive Computing 2002. • “Networked Infomechanical Systems(NIMS) for Ambient Intelligence” William J. Kaiser, Gregory J. Pottie, Mani Srivastava, Gaurav S. Sukhatme, John Villasenor, and Deborah Estrin • Maxim Batalin, Gaurav S. Sukhatme, and Myron Hattig, "Mobile Robot Navigation using a Sensor Network," To appear in IEEE International Conference on Robotics and Automation, Apr 2004. [PDF] • Maxim Batalin and Gaurav S. Sukhatme, "Using a Sensor Network for Distributed Multi-Robot Task Allocation," To appear in IEEE International Conference on Robotics and Automation, Apr 2004. [PDF]