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ENERGY: THE ROOT OF ALL PERVASIVENESS Anthony Ephremides University of Maryland April 29, 2004 1 “PERVASIVE” NETWORKING • Ability to access the network (“Anywhere,” Anytime,” “Anyone”) • Focus on wireless • More specifically: Ad Hoc (multihop) 2 KEY ELEMENTS • Wireless Channel • fading • interference • SINR <> • Portable Energy Supply • efficiency vs. • limited – ‘SOFT” – LINK GRAPHS – CROSS-LAYER COUPLING 3 TWO ILLUSTRATIONS 1. MAC/ROUTING with Energy Metrics 2. Processing vs. Transmission in Sensor Networks 4 MAC/ROUTING • Routing algorithm flows on each link • MAC assigns resources to competing flows • Actual link throughput depends on MAC • New link quality metric values • Routing Algorithm new flows 5 MULTI-HOP AD HOC NETWORK • • • • Single channel – slotted time Separate control channel Single transceiver per node Power control - Pmax – regulate interference – save energy • Simple attenuation model – free-space, distance based • SINR • > < 6 SCHEDULING Scheduling Rules: • A node can only be associated with one active link at a time. PG i ij • SIR requirements are satisfied. , link(i,j). 2 G : Attenuation fator of link(i,j). Pk Gkj ij k i • The link with the lowest metric has the top priority. Qk neighbors of i or j j 1 k i D i, j a b c i . 1 Qi 1 Qk max neighbors of i or j k i Qi : Total queue size at node i. i j : Average traffic rate at link (i,j). max : Maximum rate among all links. 7 Scheduling Algorithms: 1. Power is preset. Links are added (if SIRs are satisfied) in the order of link metric. Easy for distributed implementation. 2. With iterative power control. Links are added (if SIRs are satisfied) in the order of link metric. Difficult for distributed implementation. 3. First find maximal number of links that can coexist, then run iterative power control. Remove links until SIRs are satisfied. Difficult for distributed implementation. 8 Simulation Results No rerouting. Throughput and Delay for different scheduling algorithms. 9 JOINT SCHEDULING AND ROUTING Routing: Bellman-Ford algorithm with routing distance: Qij Dij d Qmax Rij e . Rmax Qij : Queue size at link (i,j), Qmax : Buffer size for each link, Rij : Distance between node i and j, Rmax : Maximal reachable distance. 10 CONTROL OF SENSOR NETWORKS • Application : Major Driver • But, in all cases: Longevity (energy efficiency) • Major Challenges: – MAC – Routing (map application-related objective function to link metric or MAC priority) 11 SENSOR NETWORK FOR DETECTION control node Ignore Routing Component 12 MODEL 1 2 K control center Each Node Collects Independently T independent Binary Measurements • Simplified Model 13 MODEL (cont.) Three Operating Options - Centralized : All data transmitted to CC - Distributed : Each node decides & transmits its decision to CC - Quantized : Each node sends a quantized M-bit quantity to CC where 1 M log 2 (T 1) 14 ENERGY CONSUMPTION ANALYSIS - Energy for Data Processing - based on # of comparisons EP Ec * c - E c represents the energy consumed for one comparison - c is the # of comparisons - Energy for Transmission - based on the distance from sensor nodes to control center and # of bits 2 transmitted ET Et * d * t - Et represents the energy consumed for transmitting one bit data over a unit distance, for a fixed communication system - d represents the distance from sensor nodes to control center - t is the # of bits transmitted - Total Energy E EP ET Ec * c Et * d 2 * t 15 ENERGY CONSUMPTION ANALYSIS (cont.) - Energy Consumption per Node for Three Options - Centralized Option - option 1: transmit all observations to CC E Et * d 2 * T - option 2: transmit # of 1 out of T observations to CC E Ec * T Et * d 2 *log 2 (T 1) - Distributed Option E Ec *(T 1) Et * d 2 - Quantized Option (suboptimal solution) E Ec *[T x] Et * d 2 * M where x represents the expected # of comparisons needed for the suboptimal solution, which is a function of T , M , p, p0 , p1 16 Energy Consumption Analysis (cont.) - Energy Consumption vs. Accuracy fix K 4 , p 0.5 , p0 0.2 , p1 0.7 , M 2; vary T 3 ~ 10 example 1: Ec 5 nJ bit , Et 0.2 nJ (bit * m 2 ) , d 10m example 2: Ec 20 nJ bit , Et 0.05 nJ (bit * m2 ) , d 10m 17 NEXT STEPS 1. Spatial/Temporal Correlation 2. Routing (map objective function to link metric) 3. Broader measurement model MORE FUNDAMENTALLY 1. Couple processing energy (dictated by the chosen SP algorithm) to the embedded system design. (memory management, signal flow graphs for software vs. hardware split, computing fabric) 2. Trade-off transmission to processing under such “INTERACTIVE” design (ultimate cross-layering) 18 CONCLUSIONS 1. Holistic cross-layer design from energy point of view 2. Application dependency/exploitation 3. “It takes courage to succeed” “It takes energy to be pervasive” 19