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Wireless Sensor Networks

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Presentation on theme: "Wireless Sensor Networks"— Presentation transcript:

1 Wireless Sensor Networks

2 Puzzle You are blindfolded There is a square table in front of you
Four bottles places – one at each corner Bottles can either be in UP or DOWN orientations You can “feel” any two of the bottles at a time, switch their orientation however you want to – you win if all bottles are oriented alike The table will be rotated arbitrary number of ¼ turns after each of your moves Can you guarantee that you will win?

3 Puzzle 5 moves (minimum required to guarantee)
We will denote positions of bottles as "U", "D" or "?", for "up", "down" or "unknown", respectively. We list the four positions clockwise, up to unknown rotation. Initially they are "? ? ? ?". First move: Take two opposite bottles and turn them "up". Now their setup is "? U ? U". Second move: Take two adjacent bottles and turn them "up". Now the setup is "? U U U". Assuming the referee does not stop the game, we know in fact that the setup is "D U U U". Third move: Take two opposite bottles. If one is "down", turn it "up", so that we win ("U U U U"). Otherwise they are both "up"; turn one "down", so that we are left with "D D U U". Fourth move: Take two adjacent bottles and turn them over. If they were both "up", we win with "D D D D". If they were both "down", we win with "U U U U". Otherwise our setup becomes "D U D U". Fifth move: Take two opposite bottles and turn them over, winning.

4 Project Report Due … on April 27th, by 11.59pm EST
Send electronic copy only to both TA Loren and me please pick up PA2 from TA PA3 will be given back on Monday Preliminary report can be picked up from my office Friday 12noon-2pm, or after class on Monday

5 Wireless Sensor Networks
Used for sensing! Large number of sensor nodes densely deployed either inside the phenomenon of interest or close to it Random deployment feasible due to low cost nature of sensors Sensor: sensing, processing, communication

6 WSN Organization Sensors sense, process and give information to sink
Backbone Sensing Field User/ Decision maker Sink Sensors sense, process and give information to sink Sink propagates information back to the user/decision maker

7 WSN Applications Sensors – temperature, humidity, motion, light, pressure, soil make-up, noise levels, stress, etc. Military – surveillance, targeting, damage assessment, chemical/biological agent detection Environment – forest fires, biocomplexity mapping, flood detection, precision agriculture Health – telemonitoring, tracking, drug administration Home – automation, smart environment

8 WSN vs Ad-hoc Networks Larger number of nodes Density of nodes
Failure proneness Communication patterns Limited Node capabilities Lack of Global Identifiers

9 Key Factors in WSNs Fault tolerance (survivability) Scalability
Production costs Hardware constraints Topology management

10 WSN Protocol Stack Layers Planes Application Transport Network
Data link Physical Planes Power management Mobility management Task management

11 Application Layer Time synchronization
Controlling ON/OFF decisions of sensors Querying & controlling sensor network configuration Security Task assignment Data collection

12 Transport Layer Conventional transport layer protocols (TCP, UDP, NORM, SRM, etc.) cannot be uses in a sensor network Point-to-point vs point-to-multipoint (downstream) & multipoint-to-point (upstream) Upstream: Information reliability vs data reliability Downstream: Dimensions of reliability

13 Dimensions of Reliability
All sensors in the network Sensors in a sub-region (attributes based definition) Contiguous Non-contiguous Sensors to cover sensing field (tackle redundancy) Partial reliability

14 Network Layer Data centric routing and flooding
How many pedestrians do you observe in region X? Let me know if the temperature in your local neighborhood is greater than 100F Potential data processing on paths!

15 Directed Diffusion A node requests data by sending interests for named data The request “How many pedestrians do you observe in region X” is broadcasted to region X Data matching the interest is then “drawn” down towards the node When a node in region X receives the request, it activates its sensors, and returns sensed information along reverse path of interest propagation Intermediate nodes can cache, or transform data Combine reports from multiple sensors to more accurately pinpoint pedestrian’s location

16 Elements of Directed Diffusion
Interests Query of what the user wants Data messages Collected or processed information of a physical phenomenon Gradients Direction state created in each node that receives the interest Reinforcements Of one or a small number of the available paths Example

17 Naming Attribute-value pairs Example: Vehicle detection task (query)
(Type=wheeled, interval=20ms, duration=10seconds, rect=[-100,100,200,400]) VDT (response) (type=wheeled,instance=truck,location=[125,220],intensity=0.6,confidence=0.85,timestamp=01:20:40)

18 Interests Can be initiated by the sink
Exploratory interest with a large interval, followed by reinforcements e.g. to detect any wheeled vehicles Soft-state refreshing of interests reliability & overheads Each node maintains one entry per interest in an interest-cache

19 Interests (contd.) Each interest entry contains a gradient (neighbor, report rate, and lifetime) Interest entry possibly created upon receipt of interest Interest possibly forwarded to a sub-set of neighbors e.g. based on cached data

20 Gradient Establishment
A generic notion Can be implemented in several ways: binary values, probabilistic forwarding, load balancing Gradients might be set-up differently for different tasks

21 Data Propagation Nodes in “rect” sense data
Propagates data according to the gradients to the corresponding interest entry If an intermediate node receives data, but finds on interest entry, it drops the data Gradients can change as data is being forwarded e.g. down-sampling : 100 events/second to 50 events/second

22 Reinforcement Exploratory gradients vs. data gradients
Sink reinforces one (or a subset) of the neighbors reporting back exploratory events Data gradients can have a higher reporting rate – positive reinforcement Allows sink to reinforce selective paths and reduce multi-path routing for the real heavy data

23 Other Issues MAC Topology control (with sensing reliability)
Sensor placement Reliable transport Congestion control

24 Puzzle Singly linked list Need to find if the list has a loop
Constraint1: only read operations on the nodes of the linked-list Constraint2: constant order memory usage


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