Challenges and tools for maintenance-free, intelligent distributed sensing Stephan Sigg Department of Communications and Networking Aalto University, School of Electrical Engineering stephan.sigg@aalto.fi CiNet, 22.02.2017
Stephan Sigg February 21, 2017 2 / 36
Stephan Sigg February 21, 2017 2 / 36
Stephan Sigg February 21, 2017 2 / 36
Stephan Sigg February 21, 2017 3 / 36
Stephan Sigg February 21, 2017 3 / 36
Group members and recent related research RF-based activity recognition Maintenance-free, intelligent distributed sensing Sensor graphs for distributed mathematical operation Neuron-inspired communication between distributed nodes Probabilistic superimposed mathematical operations Artificial neural computation from implicit channel inputs Conclusion Stephan Sigg February 21, 2017 4 / 36
Stephan Sigg February 21, 2017 5 / 36
Exploiting the RF-channel for environmental preception ◮ Multi-path propagation ◮ Reflection ◮ Signal superimposition ◮ Blocking of signal paths ◮ Scattering ◮ Doppler Shift ◮ Signal Phase ◮ Fresnel effects Stephan Sigg February 21, 2017 6 / 36
RF-based activity recognition Sensewaves Video Stephan Sigg February 21, 2017 7 / 36
– Video – Stephan Sigg February 21, 2017 8 / 36
RF-based device-free activity recognition g Crawling n g n i d i k n a l t a S W empty L y i n g Stephan Sigg February 21, 2017 9 / 36
RF-based device-free activity recognition g Crawling n g n i d i k n a l t a S W empty L y i n g Stephan Sigg February 21, 2017 9 / 36
Monitoring attention from RF Stephan Sigg February 21, 2017 10 / 36
Monitoring attention from RF Stephan Sigg February 21, 2017 10 / 36
Situation and gestures from passive RSSI-based DFAR 10cm 10cm Towards Away Hold over Open/close Take up Swipe Swipe Swipe Swipe Wipe No bottom top left right gesture Stephan Sigg February 21, 2017 11 / 36
Situation and gestures from passive RSSI-based DFAR 10cm 10cm Towards Away Hold over Open/close Take up Swipe Swipe Swipe Swipe Wipe No bottom top left right gesture Stephan Sigg February 21, 2017 11 / 36
Group members and recent related research RF-based activity recognition Maintenance-free, intelligent distributed sensing Sensor graphs for distributed mathematical operation Neuron-inspired communication between distributed nodes Probabilistic superimposed mathematical operations Artificial neural computation from implicit channel inputs Conclusion Stephan Sigg February 21, 2017 12 / 36
Energy-harvesting from Ambient RF noise Efficiency: DC-conversion possible at about 70% efficiency 1 7cm · 7cm rectenna : transmissions at 0.2Hz for 3.4ms each 2 0.5m 2 rectenna : RF-activity at 20Hz for 300 µ s each 3 1Doan et al. ’Design and Fabrication of Rectifying Antenna Circuit for Wireless Power Transmission System Operating At ISM Band.’ International Journal of Electrical and Computer Engineering, 2016 2Nishimoto et al. ’Prototype implementation of ambient RF energy harvesting wireless sensor networks.’ IEEE Sensors, 2010. 3Song et al. ’On the use of the intermodulation communication towards zero power sensor nodes.’ EuMC 2013 Stephan Sigg February 21, 2017 13 / 36
Maintenance-free intelligent distributed sensing Stephan Sigg February 21, 2017 14 / 36
Group members and recent related research RF-based activity recognition Maintenance-free, intelligent distributed sensing Sensor graphs for distributed mathematical operation Neuron-inspired communication between distributed nodes Probabilistic superimposed mathematical operations Artificial neural computation from implicit channel inputs Conclusion Stephan Sigg February 21, 2017 15 / 36
Neural communication for sensor networks Stephan Sigg February 21, 2017 16 / 36
Neural communication for sensor networks Problem ◮ Communication in sensor networks is omnidirectional ◮ In neural networks, the missing of edges is vital for the network’s computational power Stephan Sigg February 21, 2017 16 / 36
Neural communication for sensor networks Proposal ◮ Transmit beamforming to establish dedicated links Stephan Sigg February 21, 2017 16 / 36
Example closed-loop carrier synchronization Receiver Receiver Receiver Receiver Source Transmitter Transmitter Transmitter Transmitter Source Source Source Source Receive node broadcasts Receive nodes bounce the Receiver transmits the relative Synchronised nodes transmit common master beacon 4 beacon back on distinct phase offset of each node on these as a distributed beamformer to all source nodes CDMA channels CDMA channels to the receiver ◮ Too computationally expensive for parasitic operation 4Y. Tu and G. Pottie, Coherent Cooperative Transmission from Multiple Adjacent Antennas to a Distant Stationary Antenna Through AWGN Channels , Proceedings of the IEEE VTC, 2002 Stephan Sigg February 21, 2017 17 / 36
Example open-loop carrier synchronization ◮ Too computationally expensive for parasitic operation Stephan Sigg February 21, 2017 18 / 36
Feedback-based open-loop carrier synchron. 3 Superimposed received sum signal Receiver feedback 2 4 f 1 t + γ 1 2 π f 2 t + γ 2 2 π Mutation 2 π f n i t+ γ n 2 π f n t+ γ n i i+1 i+1 1 Iteration i Iteration i+1 y c n e u q e r 1 F 5 , 6 Time 5R. Mudumbai, G. Barriac and U. Madhow, On the feasibility of distributed beamforming in wireless networks , IEEE Transactions on Wireless Communications, 2007 6Sigg, El Masri and Beigl, A sharp asymptotic bound for feedback based closed-loop distributed adaptive beamforming in wireless sensor networks, IEEE Transactions on Mobile Computing, 2013 Stephan Sigg February 21, 2017 19 / 36
Feedback-based open-loop carrier synchronizat. ◮ Weak multimodal fitness function ◮ Single local = global optimum j G a i n ) + γ i cos( ) t i f + γ i ) ϕ 2 π 2 π f t γ i ( ϕ ( j j i e e ϕ i 1 cos( ) i ϕ j(2 π f t + γ i ) e −δ i δ i Stephan Sigg February 21, 2017 20 / 36
Feedback-based open-loop carrier synchron. Stephan Sigg February 21, 2017 21 / 36
Feedback-based open-loop carrier synchron. Stephan Sigg February 21, 2017 21 / 36
Feedback-based open-loop carrier synchron. Stephan Sigg February 21, 2017 21 / 36
Stephan Sigg February 21, 2017 22 / 36
Group members and recent related research RF-based activity recognition Maintenance-free, intelligent distributed sensing Sensor graphs for distributed mathematical operation Neuron-inspired communication between distributed nodes Probabilistic superimposed mathematical operations Artificial neural computation from implicit channel inputs Conclusion Stephan Sigg February 21, 2017 23 / 36
Calculation during transmission on the channel Envisioned paradigm shift in mobile computing Parasitic operation Communication comes virtually for free Miniaturisation Processing and storage capabilities limited (passive, parasitic, backscatter) Stephan Sigg February 21, 2017 24 / 36
Calculation during transmission on the channel Envisioned paradigm shift in mobile computing Parasitic operation Communication comes virtually for free Miniaturisation Processing and storage capabilities limited (passive, parasitic, backscatter) Potential: Trade processing load for communication load ◮ Shift computation towards the wireless communication channel Stephan Sigg February 21, 2017 24 / 36
Calculation during transmission on the channel Envisioned paradigm shift in mobile computing Parasitic operation Communication comes virtually for free Miniaturisation Processing and storage capabilities limited (passive, parasitic, backscatter) Potential: Trade processing load for communication load ◮ Shift computation towards the wireless communication channel ◮ Computation below computational complexity possible? Stephan Sigg February 21, 2017 24 / 36
Calculation during transmission on the channel Motivation: Computation during transmission a ◮ Max. rate to compute & communicate functions ◮ Mention: Collisions might contain information a A. Giridhar and P . Kumar, Toward a theory of in-network computation in wireless sensor networks, IEEE Comm. Mag., vol. 44, no 4, pp. 98-107, april 2006 Calculation of by means of post- and pre-processing a ◮ Requires accurate channel state information ◮ Requires identical absolute transmit power a M. Goldenbaum, S. Stanczak, and M. Kaliszan, On function computation via wireless sensor multiple-access channels, IEEE Wireless Communications and Networking Conf., 2009 Stephan Sigg February 21, 2017 25 / 36
Calculation during transmission on the channel Utilising Poisson-distributed burst-sequences transmit burst sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . burst . . . . . . . . . . . . . . . . . . time superimposed received burst sequence . . . . . . . . . . . . t K Stephan Sigg February 21, 2017 26 / 36
Recommend
More recommend
