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1 A Distributed Algorithm for Joint Sensing and Routing in Wireless Networks with Non-Steerable Directional Antennas Chun Zhang *, Jim Kurose +, Yong Liu ~, Don Towsley +, Michael Zink + * IBM T.J. Watson Research Center + Dept of Computer Science, University of Massachusetts at Amherst ~ Dept of Electrical & Computer Engineering, Polytechnic University Nov 14, 2006 ICNP Presented by Raymond
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2 Outline motivation problem formulation distributed algorithm result summary
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3 Multi-hop wireless sensor networks sensor nodes directional-antenna links link capacity constraints 802.11 protocol: 2/5.5/11Mbps energy constraints energy supplied by solar panel sink A sink B applications: weather monitoring performance metric amount of information delivered to sinks
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4 Interesting problem ? limited energy link capacities communication energy sensing energy sensing rate (information) radio layer application layer demand generator capacity generator more demand ? or more capacity? routing solution ? network layer
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5 Our contribution joint optimization problem formulation for energy allocation (between sensing, data transmission, and data reception), and routing distributed algorithm to solve the joint optimization problem, with its convergence proved simulation to demonstrate the energy balance achieved in a network of X-band radars, connected via point-to-point 802.11 links with non-steerable directional antennas
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6 Related work [Lin,Shroff@CDC04] joint rate control, resource allocation, and routing in wireless networks [Bin, Eryilmaz@INFOCOM06] Congestion control, distributed scheduling our work further considers energy consumption for data sensing data reception
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7 Outline motivation problem formulation distributed algorithm result summary
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8 Problem formulation In a wireless network (V, L), every node i will have a power budget p i. How to maximize information collection at sink with various energy consumption at data transmission, reception and sensing? A joint optimization problem for sensing, routing and energy consumption
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9 Resource model power resource three power usages: data sensing, data transmitting, data reception power is a convex and increasing function of data rate constraint: consumption rate ≤ harvest rate link capacity resource constraint: link data rate ≤ link capacity resource constraints satisfied by penalty functions
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10 Goal : information maximization information modeled by utility function : node i sensed and delivered data rate node i collected information assumption: is a concave and increasing function
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11 Optimization problem formulation s: sensing rates; X: data routes routes X deliver sensing rates s to data sink Joint sensing and routing problem
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12 Outline motivation problem formulation distributed algorithm result summary
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13 Main idea 3 considerations - Sensing - Routing - Energy 2 considerations - Routing - Energy Distributed solution Transform sensing into part of routing Convex optimization
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14 Transforming joint sensing/routing problem to routing problem with fixed demands i i’ wireless sensor network sensing link difference link sensing power -> reception power idea: treat data sensing as data reception through sensing link
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15 Transformed problem fixed demand: maximum sensing rates; X: data routes routes X deliver maximum sensing rates to data sink Routing problem with fixed traffic demand
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16 Distributed algorithm: generalize [Gallager77] wired network algorithm wired network link-level resource constraint wireless network node-level resource constraint How to generalize from link-level to node-level?
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17 Distributed solution Toward sink downlinks uplinks 1. Message exchange (ΔPerformance gain / Δ link usage) 2. Redistribute the data traffic (and data sensing rate) into downlinks 3. Message exchange (ΔPerformance gain / Δ link usage)
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18 Generalized distributed algorithm generalize algorithm from wired network (link-level) to wireless network (node-level) repeat, until all traffic loaded on optimal path each link locally compute gradient information gradient information propagated from downstream to upstream in accumulative manner routing fractions adjustment from non-optimal path to optimal path for generalized gradient-based algorithm: prove convergence provide step-size for routing fraction adjustment
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19 Distributed solution (con’t) Iterative process in the direction from sink to sensors Energy optimization convergence through successive rounds of message exchanges and traffic redistributions With considerations of link failure and lossy links
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20 Outline motivation problem formulation distributed algorithm result summary
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21 Simulation scenario From CASA student testbed energy harvest rate: 7-13W X-band radar-on power: 34W radar-on rate 1.5Mbps link-on trans power: 1.98W link-on receive power: 1.39W link-on goodput rate: as shown Utility function 561234 111278910 171813141516 232419202122 293025262728 1Mb 2Mb 5.5Mb 2Mb 1Mb goodput rate
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22 Optimization results for different energy harvest rates As power budget increases utility (weighted information) and sensing power increase communication power first increases, then decreases and flats out 561234 111278910 171813141516 232419202122 293025262728
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23 Node level energy balance for different energy harvest rates power budget = 9W power budget = 13W power rich network: max-min fair (single-sink) : sensing rates not affected by choice of utility functions power constrained network: close to sink nodes spend less energy on sensing 561234 111278910 171813141516 232419202122 293025262728
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24 Summary: a distributed algorithm for joint sensing and routing in wireless networks Goal : a distributed algorithm for joint sensing and routing Approach : 1.mapping joint problem to routing problem 2.proposed a distributed algorithm with convergence proof and step size Simulation to demonstrate energy balance for different energy harvest rates: 1.energy rich: proven max-min fairness (for single sink) 2.energy constrained: close-to-sink nodes spend more energy on communication, and thus less energy on sensing
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25 Comments Novelty: transform sensing energy into routing energy Data distribution (from sensors) are not well considered Applicable to periodic data collection only
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26 Thanks ! Questions ?
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