Energy Reduction in Wireless Sensor Networks

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1 Wireless Sensor Networks (WSN) Energy Reduction in Wireless Sensor Networks Olivier Sentieys with contributions from: Thomas Anger, Jérôme Astier, Arnaud Carer, Olivier Berder, Duc Nguyen, Vinh Tran, Adeel Pasha, Steven Derrien, Hai-Nam Nguyen, Daniel Ménard, Vivek T.D., Mahtab Alam IRISA/INRIA, University of Rennes 1 ENSSAT Lannion What is a Wireless Sensor Network?! Dense network of small nodes communicating through wireless links for sensing, actuation, processing, storage, communication and control ad hoc network Relay Sensor and relay Base station and gateway Wireless Sensor Network 2 Ex. Temperature sensing Wireless Sensor Networks (WSN)?(7,7) (1,8) (8,8)!(7,7)?(7,7) (7,7) (5,6) (1,5)!(7,7) (8,4)?(7,7) (4,2)!(7,7) (11,3)! Set of nodes with coordinates (X,Y)! Temperature sensing at (7,7)! BS(0,0)!?(7,7)! (1,5)!?(7,7)! (5,6)!?(7,7)! (8,8)!!(7,7)! (5,6)!!(7,7)! (4,2)!!(7,7)! (0,0) receives temperature! Simplified deployment, fault tolerance No maintenance and battery replacement! Network characteristics Low mean distance Limited amount of data Multi-hop routing! Low cost! Small size! Long autonomy, low power Towards autonomous sensor nodes (0,0) 3 4

2 On-demand model Event-driven model 5 [Simplot, 2009] [Simplot, 2009] 6 Emerging applications! Indoor environment Industrial control, machine health monitoring, condition based maintenance of equipment in factory Indoor localization Home automation, intelligent lightning and heating Health care! Outdoor environment Monitor natural habitats, remote ecosystems, agriculture, forest fires Structural Health Monitoring (e.g. bridges, buildings) Automotive Disaster sites Security & military surveillance 7 Tremendous space of applications! Monitoring space: ocean water, pollution,! Monitoring things: robots, human body, Fire detection Target monitoring Monitoring in agriculture [Fischione, UCB, 2007] 8

3 ANR SurVeiller et Prévenir Captiv Capteurs de paramètres physiologiques Suivi de l activité physique Prévention de l obésité Cooperative Wireless V2I Communications! 9 ITEA2 Geodes: fire-fighters 10 Fire-fighter scenario! Indoor network Temperature monitoring, smoke detection, motion detection Camera nodes WSN nodes Fire-Fighters FIRE-FIGHTER LOCALIZATION! Mobile nodes (firefighter) PEOPLE DETECTED Health monitoring, camera, etc. Connected with indoor network PEOPLE DETECTED FIRE DETECTED 11 PEOPLE DETECTED PEOPLE DETECTED 12

4 Challenges in WSNs (1/2) Challenges in WSNs (2/2)! Networking Physical design MAC protocols Cross-layer o Power saving, collision avoidance,... Routing protocols optimizations o Data aggregation, dissemination,...! Network management Deployment, redeployment, topology control, maintenance Localization and positioning Coverage and connectivity problems! Application Data fusion, distributed signal processing, source coding, positioning, tracking, etc.! Energy reduction of HW/SW Digital processing o Low power processors Radio front-end, analog processing o Small radios with small bandwidth & transmission ranges Light and efficient software o No complex operating systems o Mainly hardware-dependant software! Process or transmit? Limited processing power but communicating wirelessly is power-hungry! Towards a true energetic autonomy Autonomous nodes?! A WSN node is limited by the total energy it can store or scavenge from the environment Need a drastic reduction in the total consumed energy (radio + processing) [CEA Leti, Managy] 15 Agenda! WSN node architecture HW Platform Processor and radio transceiver performance! Protocols MAC and network SW Stack! Power models and estimation! Energy optimization Cross-layer (MAC/LINK) FPGA co-processing Architectural and circuit-level optimization MIMO Cooperation 16

5 Generic architecture of a node Generic architecture of a wireless node Main tasks: sense, process, store, communicate, power management Other features (e.g. location finding systems) Generator Battery DC/DC conv. Sensor A/D Processor Coprocessor Radio SW Infrastructure APPLICATION NET LINK MAC PHY RAM 17 Flash HW Infrastructure 18 Typical WSN platform! What are the main sources of energy consumption? Radio: 30-70mW Processor: 5-10mW DC/DC ucontroller Timer! Micro-controller! Transceiver in Rx! Transceiver in Tx Power consumption of a typical WSN platform Radio Typical WSN platform! Modern low-power microcontrollers Atmel Freescale Device Year Arch. Vdd (V) AT128L AT256l HCS08 MC RISC 8b b Ram (kb) 4 8 Flash (kb) Active 1 (ma) Sleep (ua) 5 1 Wake (us) Jennic JN b TI MSP430 F1612 F RISC 16b TI CC NXP LPC1114 CortexM ARM 32b Active current at 3V and 1MHz [Dutta, Sensys, 2008] 20

6 Typical WSN platform PowWow HW Platform (PWNode)! Radio transceivers IEEE compatible Device Year Vdd (V) RxSens (dbm) TxPwr (dbm ) Rx (ma) Tx (ma) Sleep (ua) Atmel RF Wake (ms) Freescale MC Jennic JN >2.5 TI CC2420 CC to 0-20 to Leti Letibee PowWow: power optimized hardware/software framework for wireless motes! Open source hardware developed at IRISA/Cairn Mother board with MSP430 Daughter boards for o V1: CC2420, Sensors o V2: FPGA, DVFS! FPGA for hardware acceleration! Voltage and frequency scaling! Power management (sleep, wake-up) #50mW PowWow HW Platform (PWNode) Agenda! Motherboard TI MSP430 low-power microcontroller o MSP430F1612 version, 8 MHz clock o 55KB of flash memory, 5KB of on-chip RAM o 330uA at 1 MHz and 2.2 V in active mode o 1.1uA in standby mode JTAG, RS232, and I2C interfaces! Radio daughterboard TI CC2420 RF transceiver single-chip 2.4 GHz IEEE compliant digital direct sequence spread spectrum baseband modem o spreading gain of 9 db, data rate of 250 kbps o hardware support for packet handling, data buffering, burst transmissions, data encryption, data authentication, clear channel assessment, link quality indication and packet timing information! WSN node architecture HW Platform Processor and radio transceiver performance! Protocols MAC and network SW Stack! Power models and estimation! Energy optimization Cross-layer (MAC/LINK) FPGA co-processing Architectural and circuit-level optimization MIMO Cooperation 23 24

7 MAC Protocols MAC Protocols! MAC protocol determines next node to use the medium! Carrier Sense Multiple Access (CSMA) Simple, scalable e.g ! Time Division Multiple Access (TDMA) Avoid collision e.g. GSM! Sensor-MAC (S-MAC) CSMA with fixed active/sleep duty cycle Synchronization of active times! Timeout-MAC (T-MAC) Make duty cycle dynamic End active time with time-out Listen always Collisions " Complexity of scheduling " Synchronization needed MAC Protocols Power Measurements on PowWow HW! Asynchronous Rendezvous Asynchronous scheme initiated by receiver RICER (Receiver-Initiated CyclEd Receiver)! Wake-up and channel sensing Rx Tx Rx Receiver wait Node j (transmitter) Node k (receiver) Wake-up period (T) Data transmission All measurements realized with Agilent N6705A DC Power Analyzer Wake-up frames Waiting time Acknowledge Radio transceiver in reception/listening mode Radio transceiver in transmission mode 27 [Lin, ICC, 2005] 28

8 Power Measurements on PowWow HW Power Measurements on PowWow HW! Wake-up and channel sensing! Wake-up and channel sensing with collision 6, 8, 16, 32, 64, 128 packet size in bytes Routing Protocols Transmission Modes! Multi-hop routing! Geographical routing Each node has (x,y) coordinates Next node for hop transmission is chosen in the neighbors as the nearest to the destination o in the sense of Euclidian distance Neighbor table management o A neighbor is a node in the radio range of another node o Set of neighbors is discovered power-up and on regular time period! Many other protocols!! Broadcast Direct transmission to {neighbors}, no ACK! Flooding Broadcast a packet to all network nodes, no ACK! Direct Hop with/without ACK Direct transmission to a specific neighbors o with or without ACK! Robust Multi-Hop Multi-hop transmission to a specific node in the network Each hop is with ACK Uses node address 31 32

9 PowWow SW Stack PowWow SW Stack! Open source software developed at IRISA/Cairn Event-driven, multi-threaded, C code Based on Protothread library and Contiki! Memory efficiency 6 Kbytes (protocol layers) + 5 Kbytes (application)! Over-the-air re-programmation (and soon reconfiguration)! HAL, PHY, LINK, MAC, NETW layers and API FEC/ARQ, geographical routing, positioning, Tx power and low-power mode management Modes: broadcast, flooding, direct/multi-hop with/without ACK Configurable packet structure! Analytical power estimation based on software profiling and power measurements of a set of scenarios! Comparison with /ZigBee More than 12 less power for the same application scenario o Temperature sensing, s sensing period, TI MAC vs. PowWow Agenda Energy Model! WSN node architecture HW Platform Processor and radio transceiver performance! Protocols MAC and network SW Stack! Power models and estimation! Energy optimization Cross-layer (MAC/LINK) FPGA co-processing Architectural and circuit-level optimization MIMO Cooperation o PTB/PRB: power in baseband digital signal processing circuit of transmitter and receiver (mw) o PTRF/PRRF: power in front-end circuit of transmitter and receiver (mw) o PA: power of the Power Amplifier (mw) o PL: power of the Low Noise Amplifier (mw) 35 36

10 Energy Model Energy Model! Total power consumption! Relation between PT(d) and the required RF output power for reliable transmission PTx(d) Tx: PT(d) for a transmission of distance d Rx: PR d 37 Chipcon Radio Tranceivers Gt, Gr: transmitter and receiver antenna gain PRx: desired receive power at destination!: drain efficiency!: carrier wavelength L: system loss factor 38 Energy Consumption! CC2420 radio transciever d=10m, SISO 39 d=100m, SISO 40

11 Energy Estimation! How to estimate the energy more precisely?! Total energy dissipated in the WSN E = V. (E p + E rt.n rt + E wuc.n wuc + E c.n c ) V = {V i,j }: volume of data between i and j N rt : mean number of retransmissions N wuc : mean number of wake-up collisions N c : mean number of collisions E p : energy of a packet transmission E rt E wuc E c : energy of a retransmission, a wake-up collision and a collision Energy Estimation! How to estimate the energy more precisely?! Mixed estimation method Profiling of code execution or platform power measurement o E p E rt E wuc E c Application and network simulation o {V i,j } Analytical performance models o N rt N wuc N c 41 [Thèse Cartron, 2006] 42 Scenario-based power estimation! Analytical approach based on software profiling and power measurements of a set of scenarios e.g. MAC events Tx Rx PP1 PP2 Too many errors detected that can not be corrected TxOK RxEr RxC Wake-up collision at Rx TxOK RxOK Wake-up collision of a Tx with another Tx 43 TxC PP2 PP1/2: Process Packet Phase! TxOK/RxOK: Normal Tx/Rx RxEr: Rx Packet with error TxC/RxC: Tx/Rx with collision RxOK TxOK Scenario-based power estimation Energy consumption of event cycles [J] CBT T WUR WUC DC TIM Rx soft 5.3e-8 5.3e-8 1.2e-4 1.2e-4 5.3e-8 5.3e-8 Tx soft 4.1e-8 1.2e-3 1.0e-8 4.1e-8 6.9e-4 4.1e-8 clock 5.5e-7 5.5e-7 5.5e-7 5.5e-7 5.5e-7 5.5e-7 LINK 4.8e e NETWORK 4.8e Req_neighb Ans_neighb positionning listen target scheduler 3.7e-7 3.7e-7 3.7e-7 3.7e-7 3.7e-7 3.7e-7 Tx HF e e Rx HF 0 5.2e-2 3.1e-3 3.1e-3 5.2e-2 0 CBT: Calculation Before Transmission T: Transmission WUR: Wake Up with Reception WUC: Wake Up with Collision DC: Data Collision TIM: Timer 44

12 Scenario 1 Scenario 2! Periodic sensing and data transmission to BS V : matrix of messages i!j T obs : simulation time F ech : sensing frequency 5 7 Periodic sensing Data transmission MSdesc V (7) MSdesc (i) : matrix of data transmitted from node i! Periodic sensing with data transmission in case of alarm Periodic sensing Alarm Alarm data E(i) : number of alarm at node i Scenario 3 Results on scenario 2! On-demand sensing! Periodic sensing with data transmission in case of alarm Query Sensing Data F(i) : number of queries to node i MSasc (i) : matrix of queries transmitted to node i No event Wake-up interval: 2 s Available energy: 3 AA batteries P = 3 mw Auton. = 126 days 0,343 0,139 1,86 3,744 Radio Rx Radio Tx 2,688 Digital Total energy: W.h 0,29 0, ,256 soft Rx soft Tx clock soft link layer soft network request neighb. answer neighb. thread positioning thread sensing scheduler radio Tx radio Rx

13 Results on scenario 2 Simulation: WSim+WSNet! Periodic sensing with data transmission in case of alarm 10 events per second Wake-up interval: 2 s Available energy: 3 AA batteries P = 5.7 mw Auton. = 66 days 13,296 Radio Rx 0,337 0,355 1,758 Digital Total energy: W.h 0,274 0, ,187 2,957 Radio Tx soft Rx soft Tx clock soft link layer soft network request neighb. answer neighb. thread positioning thread sensing scheduler radio Tx radio Rx! Open source software developed at INSA Lyon! WSim: hardware platform simulation Cycle accurate simulation Several models (processors, transceivers, peripherals) Interaction with WSNet for distributed network simulation Application final binary! WSNet: event-driven simulator for wireless networks Node simulation Environment simulation Radio medium simulation Extensibility! 49 [Chelius, 2006] 50 Simulation: WSim+WSNet Agenda! WSN node architecture HW Platform Processor and radio transceiver performance! Protocols MAC and network SW Stack! Power models and estimation! Energy optimization Cross-layer (MAC/LINK) FPGA co-processing Architectural and circuit-level optimization MIMO Cooperation 51 52

14 Main Goals Power optimization of a wireless node! How to design and optimize an energy-efficient software and hardware platform for wireless sensor networks?! (1) Decrease transmission (Tx) power Power-aware signal processing Error detection and correction! (2) Optimize radio activity and MAC SW Infrastructure APPLICATION NET LINK MAC! (3) Power optimization of the hardware 53 PHY HW Infrastructure 54 Results on MAC parameter optim. Results on MAC parameter optim.! Wake-up period influence R E 0,2 s 19,45 mw 19,3 Days T R Short WUP More collisions Useless wake-up! Wake-up period influence Application dependent T R Short WUP More collisions Useless wake-up 1,6 s 5,64 mw 66,5 Days R E R T Optimal WUP R T Optimal WUP E T Long WUP Rx power increase T Long WUP Rx power increase R 8,0 s 12,17 mw 30,8 Days R Transmission Mode Receiver Mode R Transmission Mode Receiver Mode 55 [Thèse Cartron, 2006] 56

15 Output Radio Power Management Power optimization of a wireless node TION APPLICA NET SW e ructur t s a r f n I LINK MAC PHY ture rastruc f n I W H Performance/energy joint modelling Output Radio Power Management! Energy per successfully transmitted bit! Energy per successfully transmitted bit Energy per successfully transmitted bit[j] Energy per successfully transmitted bit[j] D=10 m, Pnoise=-90 dbm, 53 bytes packets d CC1020 transceiver as a function of distance and Tx power without error correction D [m] Transmission Power (PTx) [dbm] " Dynamic adaptation of output radio power depending on channel conditions, distance, etc. " RSSI or CRC as quality metrics [Sentieys, DASIP, 2007] 59 PTx [dbm] 60

16 Power optimization of a wireless node HW Platform Energy Optimization SW Infrastructure APPLICATION NET LINK MAC PHY! (1) Co-processing! (2) Dynamic Voltage Scaling! (3) Power Gated FSM! (4) Dynamic Precision Scaling, etc. Generator Battery DC/DC conv. Sensor A/D Processor Coprocessor Radio HW Infrastructure RAM Flash PowWow HW Platform (PWNode) Co-Processing with a Low Power FPGA! FPGA co-processing, Power Mode Management (PMM) and Dynamic Voltage and Frequency Scaling (DFVS) daughterboard Low-power Igloo FPGA from Actel o AGL125: 130nm, 125 kgates, 32kbits on-chip RAM, 1 kbits Flash, PLL for clock management. o Supply voltages 0 to 1.65V o Power consumption: 2.2uW, 16uW, 1-30mW in sleep, freeze, run modes o e.g. Viterbi implemented for error correction: 5mW CC2420 Watch Dog Igloo FPGA Wake-Up Control Data DC/DC MSP430 Vdd scaling Sensors Px Co-processing mode 63 64

17 Dynamic Voltage Scaling (1/3) Dynamic Voltage Scaling (2/3) DVS (and frequency) 1.6V, 1.8V, 2V, 2.5V, 3V, 3.3V Power of MSP V, 1.8V, 2V, 2.5V, 3V, 3.3V Dynamic Voltage Scaling (3/3) PowWow HW Platform (PWNode) Power of MSP+CC V, 1.8V, 2V, 2.5V, 3V, 3.3V! FPGA/DFVS daughterboard (cont.) Power Mode Management o Low-Power Programmable Timer MAX6370, 8uA Wake-up period DVFS o Programmable Clock LTC6930, 490uA 8MHz divided by 1 to 128 o Programmable DC/DC conv. TPS62402/TPS

18 HW Platform Energy Optimization Power Gated Controllers (1/6)! (1) Co-processing! (2) Dynamic Voltage Scaling! (3) Power Gated FSM! (4) Dynamic Precision Scaling, etc.! Power Gating Principle Generator Battery DC/DC conv. Sensor A/D Processor Coprocessor Radio RAM Flash Power Gated Controllers (2/6) Power-gated Micro-Task (3/6)! Task graph to gated FSM! HW specialization! Leakage reduction! Micro-Task Customized FSM + minimalistic data-path Task A Task B Task C 71 [Pasha, ISCAS 2009] 72

19 Power Gated Controllers (4/6)! Power gain versus MSP430 software execution o Gains w.r.t. the power and energy consumptions of an MSP430F21x1 (datasheet) and an open core MSP430-clone (without memory) o Operating frequency of 16 MHz Automatic generation flow (5/6)! C to VHDL compilation flow for the automatic hardware task generation Based on GeCoS compiler/hls infrastructure 73 [Pasha, DAC, 2010] 74 Automatic generation flow (6/6)! System-level DSL Dynamic Precision Scaling (1/3)! Energy consumption reduction by fixed-point adaptation of the data-path wordlegth depending on observed error rate (BER) and signalto-noise ration (SNR) System System inputs System outputs f fp ( p) Fixed-point specification selection p Metric p measurement 75 [Thèse H.N. Nguyen] 76

20 Dynamic Precision Scaling (2/3) Dynamic Precision Scaling (3/3)! Range estimation: determine the minimal integer word-length which guarantees no overflow! Precision Analysis: determine the minimal fractional word-length which guarantees the performance criterion! 40% of energy savings between 0dB and 25dB CDMA receiver, data-path energy 77 Radio transceiver optimization [Nguyen, ISCAS, 2009] 78 Power optimization of a wireless node! LetiBee chip (CEA LETI) Expected power consumption (2nd release) Function RX (ma) TX RF LO 4 7 PLL Analog Digital Biasing TION APPLICA NET SW e ructur Infrast o Tx # dbm o Rx # dbm MAC PH Y! Trends ture rastruc f n I W H Wake-up radio, Ultra-Wide Band LINK [Bernier, ESSIRC, 2008] 79 80

21 Cooperative techniques Alamouti scheme! Context Physical layer (with impact on MAC/NET layers) Cooperative strategies between wireless nodes o take advantage of channel spatial and temporal diversity to decrease the radio output power! Model s(1) -s * (2) Source s(2) s * (1) Destination y(1) y(2)! Objectives Optimize different Cooperative MIMO techniques Compare and associate them with Relay techniques Consider the energy consumption to determine the optimal selection scheme! Protocol 81 [Alamouti 1998] 82 Performance of Space-Time Codes Cooperative MIMO! Three phases of C-MIMO communications Phase 1: Local data exchange and space-time coding Phase 2: Virtual MIMO transmission Phase 3: Cooperative reception d N r S MIMO transmission D d m N t Space-Time Codes bring us better performance 83 d m <<d d m = m 84

22 Energy consumption of C-MIMO Cooperative MIMO! Cooperative MIMO technique is more energy efficient than SISO and multi-hop SISO techniques for long distance transmission! Total energy = Transmission Energy + Digital Energy + Cooperation Energy d=100m, MISO 2-1 d=100m, SISO 85 [Nguyen, ICC, 2008] 86 Relay Technique Performance of relay technique! Diversity gains by sending additional copies of the signal through relays! Less delay, simpler processing! Two main types Amplify and Forward (AF) Decode and Forward (DF) Performance of relay model is better than that of SISO 87 88

23 MIMO relay model r 1 (1) r 1 (2) y r1 (1) y r1 (2) MIMO simple cooperative relay model r 1 (1) -r 1* (2) R1 y r1 (1) y r1 (2) Time Slot S1 S2 R1 R2 D Source s(1) s(2) y r1 (1) y r2 (1) y(1) t 1 s(1) -s * (2) s(2) s * (1) -s * (2) s * (1) y r1 (2) y r2 (2) y(2) t 2 R2 r 2 (1) r 2 (2) y r2 (1) y r2 (2) t 3 t 4 Transmit r 1 (1) Transmit r 1 (2) y(3) y(4) D y(1) y(2) y(3) t 5 y(4) y(5) y(6) Transmit r 2 (1) Transmit r 2 (2) y(5) y(6) t 6 s(1) -s * (2) Source s(2) s * (1) Time Slot S1 S2 R1 R2 D y(1) y(2) y(3) y(4) D R2 y r2 (1) y r2 (2) r 2 (2) r 2* (1) t 1 t 2 t 3 t 4 s(1) -s * (2) s(2) s * (1) y r1 (1) y r1 (2) r 1 (1) -r 1* (2) y r2 (2) y r2 (2) r 2 (2) r 2* (2) y(1) y(2) y(3) y(4) Performances! AF protocol Energy simulation! Optimal model choice to minimize energy BER (Bit Error Rate) relative distance: d(source-relay) d(source-dest) SNR (Signal to Noise Ratio) 92 [Thèse V. Tran] 93

24 Cooperative MIMO and Relay! Advantage Energy efficiency for o long-range transmission (WSN) o fading channels! Trade-off Extra circuit consumption o MIMO digital signal processing Delay of cooperative local data transmission or relay Distance between cooperating nodes d m much smaller than transmission distance d! Challenges Link with MAC and route protocols Choice of the optimal cooperating strategy 94 Summary! Energy minimization in WSN Complex cross-layer problem Power/performance models! Reduction of Tx Power Signal processing, error correcting code! Reduction of Rx activity MAC, routing! Power optimization of heterogeneous platforms Power management, dedicated hardware, power gating, etc.! Power optimization of analog and radio 95 Design challenges Bibliography Signal Processing Control! Hybrid systems! Networked control! Source coding! Modulation! MAC! Routing! Channel coding! MIMO, relay Communications WSN Computer Science! Verification! Distributed computing! Embedded software! Middleware! Operating Systems! Micro-architecture! CAD tools! Digital! Analog! RF Microelectronics [Min02] R. Min et al., Power-aware Wireless Microsensor Networks, in Power-aware Design Methodologies, [Cui04] S. Cui, A. Goldsmith, Energy_efficiency of MIMO and cooperative MIMO Techniques in Sensor Networks, IEEE JSAC, [Cartron 2006] M. Cartron, Vers une plate-forme efficace en énergie pour les réseaux de capteurs sans fil, PhD Thesis, University of Rennes 1, [Li07] Y. Li, B. Bakkaloglu and C. Chakrabarti, A System Level Energy Model and Energy-Quality Evaluation for Integrated Transceiver Front-Ends, IEEE Trans. on VLSI, [Nguyen07] Tuan-Duc Nguyen, Olivier Berder and Olivier Sentieys, Cooperative MIMO schemes optimal selection for wireless sensor networks, IEEE VTC-Spring, [Sentieys07] O. Sentieys, O. Berder, P. Quemerais and M. Cartron, Wake-up Interval Optimization for Sensor Networks with Rendez-vous Schemes, Workshop on Design and Architectures for Signal and Image Processing (DASIP), [Wang06] Q. Wang, M. Hempstead and W. Yang, A Realistic Power Consumption Model for Wireless Sensor Network Devices, IEEE SECON, [Pasha09] M. A. Pasha, S. Derrien, and O. Sentieys. Ultra low-power fsm for control oriented applications. IEEE International Symposium on Circuits and Systems, ISCAS 2009, pages , Taipei, Taiwan, May [Pasha10] M. A. Pasha, S. Derrien and O. Sentieys, A Complete Design-Flow for the Generation of Ultra Low-Power WSN Node Architectures Based on Micro-Tasking, Proc. of the IEEE/ACM Design Automation Conference (DAC) Anaheim, CA, USA, June [Nguyen08a] T. Nguyen, O. Berder, and O. Sentieys, Impact of transmission synchronization error and cooperative reception techniques on the performance of cooperative MIMO systems, IEEE ICC, [Nguyen08b] T. Nguyen, O. Berder, and O. Sentieys, Efficient space time combination technique for unsynchronized cooperative MISO transmission, IEEE VTC-Spring, [Lin05] E.Y Lin, J. Rabaey, S. Wiethoelter, and A. Wolitz. Receiver Initiated Rendez-vous Schemes for Sensor Networks. In Proc. of IEEE Globecom 2005, [Lin04] E.Y. Lin, J. M. Rabaey, and A. Wolisz. Power-Efficient Rendez-vous Schemes for Dense Wireless Sensor Networks. In IEEE International Conference on Communications ICC 2004, [Menard08A] D. Menard, R. Rocher, O. Sentieys, and O. Serizel. Accuracy Constraint Determination in Fixed-Point System Design. EURASIP Journal on Embedded Systems, [Menard08B] D. Menard, R. Rocher, and O. Sentieys. Analytical Fixed-Point Accuracy Evaluation in Linear Time- Invariant Systems. IEEE Transactions on Circuits and Systems I, 55(1), November

25 Questions? Thanks for their contributions to: Thomas Anger, Jérôme Astier, Arnaud Carer, Olivier Berder, Duc Nguyen, Vinh Tran, Adeel Pasha, Steven Derrien, Hai-Nam Nguyen, Daniel Ménard, Vivek T.D., Mahtab Alam and others

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