Computer Networks II Advanced Features (T )

Computer Networks II Advanced Features (T-110.5111) Wireless Sensor Networks, PhD Postdoctoral Researcher DCS Research Group For classroom use only, no unauthorized distribution

Wireless sensor networks: an introduction Network architecture Wireless sensor nodes Approaches to energy conservation G. Anastasi, M. Conti, M. Di Francesco, A. Passarella, Energy conservation in wireless sensor networks: A survey, Ad Hoc Networks, 7(3):537 568, May 2009 (http://dx.doi.org/10.1016/j.adhoc.2008.06.003)

Wireless sensor network Architecture and components Sensing field Internet Remote user Sink (Base station) Sensor Node

Wireless sensor node Architecture and components Power Generator Mobilizer Location Finding System MCU Battery DC-DC Sensors ADC Radio Memory Power Supply Subsystem Sensing Subsystem Processing Subsystem Communication Subsystem

Examples of sensor nodes: UCB Motes

Examples of sensors (i.e., transducers) Name Producer Type Power consumption STCN75 STM Temperature 0.4 mw ADXL330 Analog Devices Accel. (3 axis) 0.2 mw SHTx Sensirion Temperature/humidity 3 mw imems Analog Devices Accel. (3 axis) 30 mw 2200 and 2600 series CP18, VL18, GM60, GLV30 FCS-GL1/2A4- AP8X-H1141 GEMS Pressure 50 mw VISOLUX Proximity 350 mw TURCK Flow control 1250 mw

Telos node: board and integrated circuits

Power (mw) Wireless sensor node Breakdown of energy consumption 16 14 12 10 8 6 4 2 0 SENSORS CPU TX RX IDLE SLEEP RADIO Sending 1 bit of information is equivalent to process ~1000 instructions from as for energy consumption

Power (mw) Wireless sensor node Breakdown of energy consumption 16 14 12 10 8 6 4 2 0 SENSORS CPU TX RX IDLE SLEEP The power consumption of the sensor (transducer) is not always negligible

Wireless sensor networks Application scenarios and goals Data collection Long-term network lifetime Self organization Dense networks Multi-hop routes Interference

Energy conservation in WSNs Mostly targeted to the radio and the sensing (data acquisition) subsystems Energy Conservation Schemes for WSNs Duty Cycling Data-driven Mobility-based

Taxonomy of approaches based on duty cycling Duty Cycling Topology Control Power Management Location-driven Connectivitydriven Sleep/Wakeup Protocols Low-Duty Cycle MAC Protocols

Taxonomy of (general) sleep/wakeup protocols Sleep/wakeup Protocols On-demand Scheduled Rendez-vous Asynchronous On demand: low-power radios, radio-triggered wakeup Scheduled rendez-vous: synchronized wakeup Asynchronous: wakeup at any time

Taxonomy of MAC protocols with a low duty cycle Low-Duty Cycle MAC Protocols Time Division Multiple Access Contention-based Hybrid Time Division Multiple Access: Bluetooth, TRAMA Contention-based: IEEE 802.15.4, B-MAC, S-MAC, T-MAC Hybrid: Z-MAC, Crankshaft

Channel access based on long preambles Low-power listening Exploit transmit mode as it consumes less than receive mode Use a duty cycle for further energy savings Implemented by B-MAC and derived solutions (e.g., X-MAC) Preamble Msg

Taxonomy of data-driven approaches Data-driven Data Reduction Energy-efficient Data Acquisition In-network Processing Data Compression Adaptive Sampling Hierarchical Sampling Data Prediction Model-based Sensing

Example of data prediction: differential sending strategy Only send messages if values differ more than δ f(x) y 1 y 0 send skip skip skip skip skip send δ skip send skip skip skip skip t 0 t 0 + 2 T t

Example of data prediction: model-based strategy Send the description (representation) of the signal f(x) build and send the model signal differs from model, start over y 0 send all messages no messages t 0 t 1 t

Example of hierarchical sampling: triggered sensing in smart environments Event-triggered image capture Fall detection algorithm running at an ordinary sensor Tiered architecture with a multimedia sensor node Multimedia Sensor Prototype Ordinary Sensor (Sun SPOT) Sun SPOT (gateway) BeagleBoard IEEE 802.15.4 Logitech C905

Wireless sensor networks with mobile elements Definition and taxonomy Sparse wireless sensor networks Discovery of mobile elements M. Di Francesco, S. K. Das, G. Anastasi, Data Collection in Wireless Sensor Networks with Mobile Elements: A Survey, ACM Transactions on Sensor Networks, 8(1):7, August 2011 (http://dx.doi.org/10.1145/1993042.1993049)

WSNs with Mobile Elements Main components (Regular) sensor nodes Perform sensing as their main task Sources of data Sinks (base stations) Collect messages and use them or make them available Destination of data Support nodes Special nodes performing a specific task They exploit mobility to support network operation A network where at least one of them is mobile

Mobile Data Collectors Mobile elements that visit the network to gather data from source nodes Classification Mobile sinks Both dense and sparse WSNs Mobile relays Support nodes that provide a relay (forwarding) service between source nodes and the sink Gather data from sensors, store them and carry them to the base station (Rather) sparse WSNs

Mobile sinks Mobile Sink Mobile Sink

Mobile relays Sink (Base station) Mobile Relay Mobile Relay

Relocatable nodes Sink (Base station) Relocatable node Relocatable node

Mobile peers Sink (Base station)

Overview of data collection in WSNs with mobile elements Mobile element Start of contact End of contact Communication range of the node reached by the MS Data collection Exploits contacts between nodes Different from classic WSNs Three main phases Discovery Data transfer Routing to MEs Nodes reachable through multi-hop paths

Sparse wireless sensor networks Reference scenario and sensor states Mobile data collector timeout Sleeping Discovery timeout MDC discovered Data Transfer MDC out of reach or communication over MDC in contact with at most one sensor at any time Additional sleeping phase

Communication in sparse WNS Nodes wait for the MDC to approach and then transfer data Pros Decreased message loss Nodes do not have to relay messages Tight synchronization is not required Cons Increased latency Cost of MDCs

Discovery phase Asynchronous protocol with duty cycle Mobile data collector Emits beacon messages periodically Static sensor node Wakes up periodically to listen for incoming beacons Node T OFF T ON Active... T ON = T B + T BD MDC T D T B... δ = T ON T ON + T OFF

Evolution of sensing scenarios: from sensors to phones and things From sensors to smartphones People-centric sensing applications Internet of Things Andrew T. Campbell et al., The Rise of People-Centric Sensing, IEEE Internet Computing 12(4):12 21, July 2008 (http://dx.doi.org/10.1109/mic.2008.90) L. Atzori, A. Iera, and G. Morabito, The Internet of Things: A survey, Computer Networks, 54(15):2787 2805, October 2010 (http://dx.doi.org/10.1016/j.comnet.2010.05.010)

Wireless sensor networks with mobile elements revisited Mobile Sink Mobile Sink

From sensor devices to smartphones Smartphones as sensing platforms Abundance of sensors Acceleration Location, orientation Sound, video Proximity Rich in processing and storage resources Enabling even computationally intensive applications Several wireless technologies WiFi, Bluetooth (Low Energy) Long range cellular radio Near Field Communication (NFC)

People-centric sensing scenarios Passive sensing scenarios People and communities are characterized through data sampled by the phone during everyday life Can be seen as a special case of WSNs with MEs or DTNs Also referred to as people-based sensing Active sensing scenarios People involved in sensing campaigns Participants instructed to actively sense the environment Sample applications: traffic/accidents monitoring, well being Incentives for participation Also known as participatory sensing

The Internet of Things Networked objects (devices) Deployed worldwide Connected over the Internet IoT devices Individually addressable Interconnected and accessed through the standards of the web Not only sensors but also actuators (i.e., power switches) Major issues Heterogeneity Scale

Computer Networks II Advanced Features (T-110.5111), PhD mario.di.francesco@aalto.fi