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Published on January 15, 2008

Author: Susann

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Ubiquitous Wireless Networking:  Ubiquitous Wireless Networking Prasun Sinha http://www.cse.ohio-state.edu/~prasun Announcements:  Announcements Sep 26th class moved to Sep 27th (Thursday) same time same room Plan to attend Distinguished Lecture instead: Sep 26, 2007 3:30 pm 480 Dreese Labs Topic: “What Analytical Performance Modeling Teaches Us About Computer Systems Design” Speaker: Mor Harchol-Balter, Computer Science Dept., Carnegie Mellon University 2 Slide3:  Outline Wireless Technologies Wireless LANs & Mesh Networks 4 Active projects Sensor Networks 2 Active projects Future of Wireless Networks 3 Slide4:  Benefits of Wireless Technology Allows nodes to move while staying connected Removes cost of wiring Become instant service provider with a wireless router In some environments such as space and defense, wired infrastructure is non-existent 4 Slide5:  Wireless Networking Myths Myth #1 Adding enough error correction makes a wireless link “look” like a wired link. So wireless networking problems are similar to wired networking problems. Myth #2: It is only used in the last hop for accessing the network Myth #3: Wireless research is -- taking ideas from wired networking and using it in wireless networks Myth #4: Wireless networks are fast enough 54 Mbps ought to be enough for anybody 5 Slide6:  Wireless Networking Technologies Past: Stagnating Bluetooth Infra-Red Present: Booming Wireless LAN (WLAN) or WiFi 3rd Generation (3G) Networks Future: Promising WiMAX Mobile Ad-hoc Networks Mesh Networks Sensor Networks Max Range (meters) Data Rate (Mbps) 1 10 100 1000 0.1 1 10 100 Bluetooth WLAN 3G 10000 WiMAX 6 Slide7:  Wireless LAN or WiFi Operates in unlicensed spectrum Provides up to 54 Mbps per cell Reasons for popularity High Throughput Affordable Integrated in hand-held devices Tablet PCs: Compaq, Acer PDAs: Toshiba, Palm, Compaq Cellphones: Symbol, Spectralink Laptops: IBM, HP, Apple,Toshiba, Dell, Compaq, Gateway 7 Slide8:  Hotspots (3000 in US): Airports, Malls, Hotels, Coffee shops Megabeam, Cometa Networks (AT&T, IBM, Intel), Toshiba, China Mobilie, Meshnetworks, Boingo, Sputnik, Wayport 20% of large corporations are WLAN enabled: Gartner Group 30 million homes and offices are using WLAN : Cahners-Instat 8 707 million WiFi users by end of 2008 – Pyramid Research Hotspot market will grow to $1.4 bn by 2009 – Frost & Sullivan WiFi: Popular Deployment Styles:  WiFi: Popular Deployment Styles WiFi with Wired Backbone Mesh Network (Wireless Backbone) To Internet To Internet 9 Mesh Network:  Mesh Network Static Mobile 10 Mesh Networking Challenges:  Mesh Networking Challenges Architectures Dynamic configuration of channels and antennas Deployment for coverage and connectivity Protocols Minimize impact of interference MAC Scheduling Distributedly converge to optimum performance MAC Scheduling Routing Transport 11 Mesh Network Project M.1: Network Design for Time-Varying Channels [NSF]:  Mesh Network Project M.1: Network Design for Time-Varying Channels [NSF] Goal To design network protocols that can perform optimally in presence of time-varying channels Current Status: Designed Learn-on-the-fly protocol for identifying low latency routes based on local data-driven measurements Designed Association Control protocols for optimizing performance of Multicasting in Mesh Networks Designed Efficient MAC layer multicasting solutions in presence of time-varying channels 12 Mesh Network Project M.1: Network Design for Time-Varying Channels [NSF]:  Mesh Network Project M.1: Network Design for Time-Varying Channels [NSF] Open Problems Cooperative Downloads with Transient Seeds Dynamic radio configuration 13 Mesh Network Project M.2: Optimal Network Layer Design based on Channel Models:  Mesh Network Project M.2: Optimal Network Layer Design based on Channel Models Goal Predict the channel quality and take optimum actions at the MAC and network layers Current Status We designed the “pushback algorithm” Determines “to transmit or not to transmit” Simulations and initial performance evaluation on sensor network testbed are promising 14 Mesh Network Project M.2: Optimal Network Layer Design based on Channel Models:  Mesh Network Project M.2: Optimal Network Layer Design based on Channel Models Plain CSMA (Low CS-Threshold) Plain CSMA (High CS-Threshold) CSMA with Transmission Pushback (High CS-Threshold) Pushback period (based on channel estimation) Channel Quality Indicator (for illustration only) Poor Transmitted packet Transmitted but dropped packet Time Good Pushback Algorithm 15 Mesh Network Project M.2: Optimal Network Layer Design based on Channel Models (Contd.):  Mesh Network Project M.2: Optimal Network Layer Design based on Channel Models (Contd.) Open Problems: Joint optimization of power, transmission rate, and decision to transmit or not transmit Optimum routing decisions based on predicted channel quality Implementation and Evaluation on 802.11 testbed 16 Mesh Network Project M.3: Delay Tolerant Networking for City-Wide Wireless Coverage with Sparse Deployments:  Mesh Network Project M.3: Delay Tolerant Networking for City-Wide Wireless Coverage with Sparse Deployments Goal Provide interruption-transparent services at higher layers for enabling multimedia based applications for mobile users Current Status Just started 17 Mesh Network Project M.3: Delay Tolerant Networking for City-Wide Wireless Coverage with Sparse Deployments (contd.):  Mesh Network Project M.3: Delay Tolerant Networking for City-Wide Wireless Coverage with Sparse Deployments (contd.) Open Problems What is the notion of fairness considering that some users are moving between hotspots? Large Scale Experimentation 18 Mesh Network Project M.4: Rechargeable Mesh Networks:  Mesh Network Project M.4: Rechargeable Mesh Networks Goal To operate an outdoor energy-harvesting based mesh network considering the limitations of the remaining battery and the expectation of future recharging Current Status Working with Civil Engineering department for installing a wireless mesh network on the coast of Lake Erie for monitoring bluff erosion 19 Mesh Network Project M.4: Rechargeable Mesh Networks (Contd.):  Mesh Network Project M.4: Rechargeable Mesh Networks (Contd.) Open Problems Schedule node wakeup such that the number of nodes accessible from the sink is optimized over a period of time Operate the MAC, routing and transport layers considering predicted recharging rate and the existing battery lifetime 20 Research Area II: Sensor Networks :  Research Area II: Sensor Networks 21 The Evolution of Computers:  The Evolution of Computers 22 Wireless Sensors:  Wireless Sensors Genesis of Wireless Sensors Miniaturization of sensing and actuating components Miniaturization of computing platforms Miniaturization of wireless component An Example: Berkeley MicaDot Low battery power Low memory (8 KB program, 512 B data) Little computing capability (4 MHz processor) Low data rate (30 Kbps) Types of sensors Temperature, Pressure, Humidity, Magnetometer, Tilt sensors, Accelerometers, Acoustic, PIR (Passive Infra Red)… Berkeley MicaDot 23 Network of Sensors: Transforming the way we sense:  Network of Sensors: Transforming the way we sense Capability to sense large regions with simple devices will enable new applications Numerous Large Scale Sensor Networks has the potential to redefine the Internet Sink 24 Some Applications of Sensor Networks:  Some Applications of Sensor Networks Data Collection Networks Sensing Movement of Glaciers Environment Monitoring Habitat Monitoring Habitat Monitoring of Storm Petrels in Great Duck Island Microsoft’s SensorMap Event Triggered Networks Structural Monitoring Golden Gate Bridge Precision Agriculture Oregon and British Columbia Vineyards Condition based Maintenance Hardware Manufacturing facilities Military Applications Environment Monitoring Poisonous gas, pollutants etc. National Asset Protection Coastline, Border Patrol, Roadways, Oil/gas pipelines, Secure facilities 25 World’s Largest Sensor Network Experiment (http://ceti.cse.ohio-state.edu/exscal):  World’s Largest Sensor Network Experiment (http://ceti.cse.ohio-state.edu/exscal) OSU-led DARPA/NEST sponsored Detection, Classification and Tracking of intruders (humans, cars, SUVs) 1000 sensor nodes; 200 Stargates forming a mesh-based backbone (also world’s largest) Successful demonstration in a 1.3 km x 0.3 km area My Contribution: Protocols for large-scale 802.11 mesh network Reliable Network Reprogramming [RTSS 2005] Low latency routing for bursty traffic [INFOCOM 2006, Book chapter] System Architecture [DCOSS 2005 poster, MOBISYS 2005 poster] Sensor (outside) Sensor (inside) Linux/802.11 based Stargate Avon Park, Florida 26 Experiments on Large Scale Wireless Networks:  Experiments on Large Scale Wireless Networks Kansei Indoor Testbed, OSU: http://ceti.cse.ohio-state.edu/kansei 210 802.11 based Stargate (Linux based) nodes 210 Mica2 nodes Will be expanded to 1000 nodes this Autumn ORBIT Indoor Testbed, Rutgers: http://www.orbit-lab.org 400 PCs with dual 802.11 radio devices Noise generators to control amount of noise 27 Habitat Monitoring by Intel (Great Duck Island, Maine) http://www.greatduckisland.net/:  Habitat Monitoring by Intel (Great Duck Island, Maine) http://www.greatduckisland.net/ Goal: non-intrusive and non-disruptive monitoring of sensitive wildlife and habitats These networks monitor the microclimates in and around nesting burrows Storm Petrel 28 Questions to be Answered by Collected Data:  Questions to be Answered by Collected Data What environmental factors make for a good nest? How much can they vary? What are the occupancy patterns during incubation? What environmental changes occurs in the burrows and their vicinity during the breeding season? 29 Structural Engineering (CITRIS, UC Berkeley):  Structural Engineering (CITRIS, UC Berkeley) Goal: make buildings, bridges and other structures aware of their own health A wireless Smart Dust Mote vs. today’s accelerometers 30 Sensor Network Project S.1: Energy Efficient Protocol Design [NSF]:  Sensor Network Project S.1: Energy Efficient Protocol Design [NSF] Goal To design cross-layer solutions based on solid theoretical foundation that rigorously manages both performance and complexity with the aim of practical implementation. Current Status Anycast based MAC layer for sensor networks: CMAC Performance of anycasting 31 Sensor Network Project S.1: Energy Efficient Protocol Design [NSF] (Contd.):  Sensor Network Project S.1: Energy Efficient Protocol Design [NSF] (Contd.) Open Problems Joint Link Scheduling and Routing Sleep/Wake Scheduling In-network Aggregation/Computation Reliable Broadcast 32 Sensor Network Project S.2: Tracking with Sparse Sensor Networks [NSF]:  Sensor Network Project S.2: Tracking with Sparse Sensor Networks [NSF] Goal To establish a strong foundation for all large scale movement tracking applications and address the key systems issues faced in such applications. Current Status Initial theoretical results on a new way to deploy sensors sparsely Designed techniques for capturing quality of deployment 33 Sensor Network Project S.2: Tracking with Sparse Sensor Networks [NSF] (Contd.):  Sensor Network Project S.2: Tracking with Sparse Sensor Networks [NSF] (Contd.) Open Problems What is the optimum strategy for sparse deployment considering obstacles What should be the wakeup schedule in order to maintain the quality of tracking? How can packets be routed efficiently in the network if the network is composed of a large collection of “holes”? Initial results based on geometric modeling of the network Where should data be stored in the network for later retrieval? 34 Slide35:  Why do we need Imprecision-Tolerant Tracking? Typical applications can tolerate tracking imprecision Full coverage is prohibitively expensive for very large coverage regions Barrier Coverage Application limited to penetration detection at a boundary Not designed for tracking in a region Quality Metric for Tracking Coverage Evaders location tracked with inaccuracy of up to d. k-tracking coverage (Generalization) Detected at least once by k sensors (simultaneously) when it moves a distance of d. Key Observation Deployment can have holes with diameter at most d. Diameter (d) Last detected location Current location coverage hole k-covered Tracking Scenarios Monitoring movement of critical objects (e.g: IEDs and RDX) in NYC Intruder tracking Battlefield US Border Patrol (Land and Ocean) Monitoring movement patterns of endangered animals (land and aquatic) Sensor Network Project S.2: Tracking with Sparse Sensor Networks [NSF] (Contd.) 35 Sensor Network Project S.2: Tracking with Sparse Sensor Networks [NSF] (Contd.):  Optimal Planned Deployment Given a region to cover, what is the optimal number and location of additional sensors to provide k-d tracking coverage Approach: Hexagonal tessellation (for some special cases) Given a deployment of existing nodes, what is the optimal number and location of additional sensors to provide k-d tracking coverage Approach: Identify k-coverage islands Critical Conditions for Random Deployment Critical conditions relating k, d, r (sensing radius) and N (numer of sensors) Deployment Characterization Is it k-d covered? Can it be determined by local communication? Given k, what is max d for which k-d covered (and vice versa) Lifetime Optimization For given deployment, k and d, what is the maximum network lifetime? What is the sleep wakeup schedule to achieve it? What if the battery lifetime is non-uniform for the nodes? Sensor Network Project S.2: Tracking with Sparse Sensor Networks [NSF] (Contd.) 36 Future of Wireless Networking (commercial applications beyond Internet connectivity):  Future of Wireless Networking (commercial applications beyond Internet connectivity) IP picture frame http://www.ceiva.com/ Web-enabled toaster+weather forecaster Marine Soil Water content Moisture content 37 Future of Wireless Networking (non-commercial):  Future of Wireless Networking (non-commercial) 38 Thanks!:  Thanks! For further information please visit: http://www.cse.ohio-state.edu/~prasun Note: I am looking for MS/PhD students with strong mathematical inclination and background. 39

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