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Introduction to Distributed Systems: Sensor Network © Marcus Chang 2007
What is a Sensor Network? Traditional sensor usage: Sensors connected to a single processing unit E.g. a fire alarm has smoke detectors connected to a master control box Sensor Network: Network of nodes, each equipped with processing, communication and sensors: Sensor Nodes Vision: Thousands of autonomous nodes, solving a task in collaboration © Marcus Chang 2007
Terminology © Marcus Chang 2007
Why? Scientific data collection: Higher density deployment of sensors Autonomous and unattended operation Data management Redundancy Ubiquitous computing: Home automation Reactive environment Healthcare Problems: How do you change batteries in the field? Who can afford thousands of sensor nodes? © Marcus Chang 2007
What characterizes a sensor node? Key constraints: Power consumption and unit cost Limited processing: Low frequency, no floating point, 8/16 bit Limited communication: Low bandwidth Limited storage: Size and cost an issue © Marcus Chang 2007
Numbers game – Micro4 CPU: 16 bit, 0-8 MHz RAM: 10 KiB Flash: 768 KiB Radio: 250 KB/s Lifetime on AA batteries : 4 days © Marcus Chang 2007
Numbers game – Nano CPU: 8 bit, 0-32 MHz RAM: 8 KiB Flash: 128 KiB Radio: 250 KB/s Lifetime on AA batteries : 4 days © Marcus Chang 2007
Numbers game - performance © Marcus Chang 2007 Lz77 compression of 4 KiB of data
Things to consider about sensor nodes Limited hardware: No or limited OS support (Race conditions, memory management, process switching) No floating point No file system Only light-weight algorithms Physical world interaction: Errors and failures are the rule Deployment and configuration © Marcus Chang 2007
Things to consider about energy Power consumption: Only turn components on when needed (duty-cycling) Radio: Only transmit when others are listening Only listen when others are transmitting Response time Solution: Advanced MAC-protocols © Marcus Chang 2007
Fact or Fiction? Sensor nodes based on consumer class hardware: Standard components, performance and protocols High power consumption Expensive Rare Miniaturized sensor nodes: Short term field experiments Network with +100 of nodes Home automation © Marcus Chang 2007
The Future is Now PODSS (Portable Overt Digital Surveillance System) Deployment: +100 in Chicago Communication: 802.11x (mesh network) Sensors: audio & visual Size: 2 feet Unit price: $25,000 Autonomous: 1.Gunshot heard 2.Triangulate direction 3.Aim camera 4.Alert 911 5.Stream video to officers on route © Marcus Chang 2007 http://www.podss.net
The Future in the Future Hardware trend: Smaller size, cheaper cost, less power comsumption Scientific computing (e-science): Long term field deployments Self-configuration Online data access Thousands of nodes Ubiquitous computing: Intelligent environment © Marcus Chang 2007
Distributed Systems Sensor Network viewed as a Distributed System: Concurrency: Nodes operate in parallel Independent, individual component failure: Node crash, network partitioning No global time: No implicit ordering of events Security: No implicit trust between components Many of the same techniques and algorithms still apply but the design goals and constraints can differ greatly © Marcus Chang 2007
Communication Distributed system: Message parsing Remote procedure calls Sensor Network: Medium-Access-Control (MAC): Limited range, data rate and power Broadcast medium Duty-cycling Routing: Direct addressing vs. content addressing Peer-to-peer vs. master/slave Network topology © Marcus Chang 2007
Typical Topologies Sensor Network: Fully Connected Tree Star Mesh Line Combinations: Mesh of stars Tree routing in a mesh topology... © Marcus Chang 2007
MAC: Scheduled Communication 1.Define repeating time period 2.Divide period in time slots Time slot usage: First-come-first-serve Transmit Receive Note: Time synchronization Trial and error © Marcus Chang 2007
MAC: Power Efficient Discovery Callee: Periodic listening Caller: Initiates communication Transmits beacon longer than period Listens for response after beacon Callee:Responds to beacon Link established © Marcus Chang 2007 Callee node: Caller node: Listen Transmit Link
Contents based addressing: source/sink (producer/consumer) Peer-to-peer: mesh network – tree routing Routing: Directed Diffusion © Marcus Chang 2007 Sink Source Interest propagation Gradient set up Message routing Network link
Content: source/sink (producer/consumer) Peer-to-peer: mesh network – tree routing 1.Interest propagation (broadcast/flooding) Routing: Directed Diffusion © Marcus Chang 2007 Sink Source Interest propagation Gradient set up Message routing Network link
Sink Content: source/sink (producer/consumer) Peer-to-peer: mesh network – tree routing 1.Interest propagation (broadcast/flooding) 2.Gradient set up Routing: Directed Diffusion © Marcus Chang 2007 Source Interest propagation Gradient set up Message routing Network link
Content: source/sink (producer/consumer) Peer-to-peer: mesh network – tree routing 1.Interest propagation (broadcast/flooding) 2.Gradient set up 3.Data delivery (tree routing) Routing: Directed Diffusion © Marcus Chang 2007 Sink Source Interest propagation Gradient set up Message routing Network link
Reliability and Fault-tolerance Retransmission: Acknowledgements Timeouts Replication: Multiple routes Redundant routers Issues: Asymmetric links Duplicate message recognizition © Marcus Chang 2007
Real time clock? How does a sensor node keep time? Clock generator (square wave) Internal circuitry (MHz range), ms 16 MHz crystal, us, high power 32 kHz crystal, ms, low power Clock counter: 8/16 bit integer Extendable in software I.e. a sensor node counts the number of ’ticks’ since power on © Marcus Chang 2007
Synchronization Same principles as normal Distributed Systems: Christians algorithm Logical clocks etc. Considerations: Overhead vs. usage vs. needed precision Duty-cycling of clock sources: Different clocks have different precision Re-synchronization – how often? Post processing: Convert logical clock to real-time clock © Marcus Chang 2007
The Power of Broadcast 1. Client node synchronizes with Server node (e.g. Christians) © Marcus Chang 2007 clientserver client server mrmr m n mrmr
1. Client node synchronizes with Server node (e.g. Christians) 2. All nodes receive m t at the same time due to broadcast mtmt mtmt mtmt The Power of Broadcast © Marcus Chang 2007 clientserver client server mrmr mtmt tctc m n t c : m t t m : m t t n : m t t c ~ t m ~ t n
The Power of Broadcast 1. Client node synchronizes with Server node (e.g. Christians) 2. All nodes receive m t at the same time due to broadcast 3. Client calculates offset to message from Server 4. Client broadcasts offset to other nodes in range © Marcus Chang 2007 clientserver client server mrmr mtmt tctc m n offset t c = m t + offset
Large scale broadcasting Time beacon: Radio tower broadcasts time beacons Nodes in range become synchronized to same local time Europe: DCF77 (micro second) a.k.a. Frankfurt clock a.k.a. Radio controlled clock World: GPS (nano second) © Marcus Chang 2007
Summary Sensor Network: Network of nodes, each equipped with processing, communication and sensors: Sensor Nodes Power consumption: Components only available in a short time frame (duty-cycling) Processing power: Normal algorithms can be too computation intensive Physical interaction: Volatile system – errors will occur © Marcus Chang 2007
Sensor Network: Case studies 1.Sow monitoring (DIKU) 2.Fall prevention (DIKU) 3.Fall detection 4.Lake monitoring (DIKU) © Marcus Chang 2007
Case 1: Hogthrob – Sow monitoring Sensor node: Micro4 3-axis accelerometer Field experiment: Sampling rate: 4 times each second Lifetime: 30 days Duty-cycling strategy: Store at least 256 KiB and transmit in bulk Compression vs. radio bandwidth © Marcus Chang 2007
Infrastructure Server 2 Gateways: PC + Micro4 Internet Star network Biologist © Marcus Chang 2007
Usage Heat detection: Activity: Calculated from acceleration Cécile Cornou et al: “Oestrus Detection for Group Housed Sows” Normal : In heat: © Marcus Chang 2007
Usage Animal wellfare: Disease and injury detection Vertical acceleration profiles: © Marcus Chang 2007
Case 2: Pig monitoring spin-off: Healthcare Monitoring elder people: Fall prevention Sensor node: Nokia 5500 Sport 3-axis accelerometer Event detection: Real-time data mining at backend server © Marcus Chang 2007
Event detection – how? 1.Meassure acceleration 2.Fourier transform 3.Detect pattern © Marcus Chang 2007 “Acceleration patterns of the head and pelvis when walking on level and irregular surfaces”, H. B. Menz, S. R. Lord, and R. C. Fitzpatrick, Gait and Posture, 18. 35-46. 2003. Vertical acceleration:
Case 3: Fall detection – commercially available http://www.tunstall.co.uk Smoke alarm IR sensor Panic buttons Pull cord Fall detector Call center Base station Pressure mat © Marcus Chang 2007
Case 4: Lake monitoring in Greenland MANA Project: 2008-2011 What: Temporal variation of chlorophyll in fresh water lakes Now: Biologist collects a bucket of water, a few times a year Goal: Underwater chlorophyll sensor nodes Daily measurements Data quality high enough for scientific work Problems: Harsh, remote environment © Marcus Chang 2007
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