Wireless Sensor Networks

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

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

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

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

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

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

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

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

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!

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

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

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)

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

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

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

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 no 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

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

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

Puzzle Three bulbs inside a room Three switches outside the room Room initially locked You can initially play with the switches You then need to enter the room and be able to match switches to their respective bulbs Devise a strategy