Routing and Data Dissemination

Outline Motivation and Challenges Basic Idea of Three Routing and Data Dissemination schemes in Sensor Networks Some Thoughts on Comparison of the Data dissemination schemes

Differences with Current Networks Difficult to pay special attention to any individual node: Collecting information within the specified region Collaboration between neighbors Sensors may be inaccessible: embedded in physical structures. thrown into inhospitable terrain.

Differences with Current Networks Sensor networks deployed in very large ad hoc manner No static infrastructure They will suffer substantial changes as nodes fail: battery exhaustion accidents new nodes are added.

Overall Design of Sensor Networks Internet technology coupled with ad-hoc routing mechanism  Each node has one IP address  Each node can run applications and services  Nodes establish an ad-hoc network amongst themselves when deployed  Application instances running on each node can communicate with each other

Why Different and Difficult? Content based and data centric Where are nodes whose temperatures will exceed more than 10 degrees for next 10 minutes? Tell me the location of the object ( with interest specification) every 100ms for 2 minutes.

Why Different and Difficult? Multiple sensors collaborate to achieve one goal. Intermediate nodes can perform data aggregation and caching in addition to routing. where, when, how?

Why Different and Difficult? Not node-to-node packet switching, but node- to-node data propagation. High level tasks are needed: At what speed and in what direction was that elephant traveling? Is it the time to order more inventory?

Challenges Energy-limited nodes Computation Aggregate data Suppress redundant routing information Communication Bandwidth-limited Energy-intensive Goal: Minimize energy dissipation

Challenges Scalability: ad-hoc deployment in large scale Fully distributed w/o global knowledge Large numbers of sources and sinks Robustness: unexpected sensor node failures Dynamically Change: no a-priori knowledge sink mobility target moving

Challenges Topology or geographically issue Time : out-of-date data is not valuable Value of data is a function of time, location, and its real sensor data. Is there a need for some general techniques for different sensor applications? Small-chip based sensor nodes Large sensors, e.g., radar Moving sensors, e.g., robotics

Directed Diffusion A Scalable and Robust Communication Paradigm for Sensor Networks C. Intanagonwiwat R. Govindan D. Estrin

Application Example: Remote Surveillance e.g., “Give me periodic reports about animal location in region A every t seconds” e.g., “Give me periodic reports about animal location in region A every t seconds” Tell me in what direction that vehicle in region Y is moving? Tell me in what direction that vehicle in region Y is moving?

Basic Idea In-network data processing (e.g., aggregation, caching) Distributed algorithms using localized interactions Application-aware communication primitives expressed in terms of named data

Elements of Directed Diffusion Source and Sink Data Data is named using attribute-value pairs Interests Record indicating desire of certain types of information is called interest Gradients Gradients is set up within the network designed to data matching the interest.

Naming Content based naming Tasks are named by a list of attribute – value pairs Task description specifies an interest for data matching the attributes Animal tracking: Interest ( Task ) Description Type = four-legged animal Interval = 20 ms Duration = 1 minute Location = [-100, -100; 200, 400] Request Node data Type =four-legged animal Instance = elephant Location = [125, 220] Confidence = 0.85 Time = 02:10:35Reply

Interest The sink periodically broadcasts interest messages to each of its neighbors Every node maintains an interest cache Each item corresponds to a distinct interest No information about the sink Interest aggregation : identical type, completely overlap rectangle attributes Each entry in the cache has several fields Timestamp: last received matching interest Several gradients: data rate, duration, direction

Source Sink Interest = Interrogation Gradient = Who is interested ( data rate, duration, direction ) Setting Up Gradient Neighbor’s choices : 1. Flooding 2. Geographic routing 3. Cache data to direct interests

Data Propagation Sensor node computes the highest requested event rate among all its outgoing gradients When a node receives a data : Find a matching interest entry in its cache Examine the gradient list, send out data by rate Cache keeps track of recent seen data items (loop prevention) Data message is unicast individually to the relevant neighbors

Source Sink Reinforcing the Best Path Low rate eventReinforcement = Increased interest The neighbor reinforces a path: 1. At least one neighbor 2. Choose the one from whom it first received the latest event (low delay) 3. Choose all neighbors from which new events were recently received

Local Behavior Choices  For propagating interests  In the example, flood  More sophisticated behaviors possible: e.g. based on cached information, GPS  For setting up gradients  data-rate gradients are set up towards neighbors who send an interest.  Others possible: probabilistic gradients, energy gradients, etc.

Local Behavior Choices For data transmission Multi-path delivery with selective quality along different paths Multi-path delivery with selective quality along different paths probabilistic forwarding single-path delivery, etc. For reinforcement reinforce paths based on observed delays reinforce paths based on observed delays losses, variances etc.

Initial simulation study of diffusion Key metric Average Dissipated Energy per event delivered indicates energy efficiency and network lifetime diffusion Compare diffusion to flooding flooding omniscient multicast centrally computed tree (omniscient multicast)

Diffusion Simulation Details ns-2 Simulator: ns-2 Network Size: Nodes Transmission Range: 40m Constant Density: 1.95x10 -3 nodes/m 2 (9.8 nodes in radius) MAC: Modified Contention-based MAC Energy Model: Mimic a realistic sensor radio [Pottie 2000] 660 mW in transmission, 395 mW in reception

Diffusion Simulation Surveillance application 5 sources are randomly selected within a 70m x 70m corner in the field 5 sinks are randomly selected across the field High data rate is 2 events/sec Low data rate is 0.02 events/sec Event size: 64 bytes Interest size: 36 bytes

Average Dissipated Energy Average Dissipated Energy (Joules/Node/Received Event) Network Size Diffusion Omniscient Multicast Flooding Diffusion can outperform flooding and even omniscient multicast.

Comments Primary concern is energy Simulations only Only use five sources and five sinks How to exam scalability?