Routing and Data Dissemination Vinay Singh Dongseo University

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.

Differences with Current Networks User and environmental demands also contribute to dynamics: Nodes move Objects move Data-centric and application-centric Location aware Time aware

Overall Design of Sensor Networks One possible solution? 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? A sensor node is not an identity (address) 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?

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

Broadcast with minimum energy W.R.Heinzelman, J.Kulik, H.Balakrishnan SPIN: The Goal Broadcast with minimum energy W.R.Heinzelman, J.Kulik, H.Balakrishnan

Conventional Approach B D E F G Flooding Send to all neighbors E.g., routing table updates

Resource Inefficiencies Implosion A B C D (a) A B C (r,s) (q,r) q s r Data overlap Resource blindness

What is the optimum protocol? B D E F “Ideal” Shortest-path routes Avoids overlap Minimum energy Need global topology information G

Two basic ideas Exchanging sensor data may be expensive, but exchanging data about sensor data may not be. Nodes need to monitor and adapt to changes in their own energy resources

SPIN Family Sensor Protocol for Information via Negotiation Data negotiation Meta-data (data naming) Application-level control Model “ideal” data paths SPIN messages ADV- advertise data REQ- request specific data DATA- requested data Resource management ADV A B REQ A B DATA A B

SPIN-PP Example: DATA ADV A REQ DATA ADV REQ B

SPIN on Point-to-Point Networks SPIN-PP 3-stage handshake protocol Advantages Simple Minimal start-up cost SPIN-EC SPIN-PP + low-energy threshold Modifies behavior based on current energy resources

Test Network 16 bytes 500 bytes 25 Nodes 59 Edges Average degree = 4.7 neighbors Network diameter = 8 hops Data Antenna reach = 10 meters Meta-Data

Unlimited Energy Simulations -- SPIN-PP -- Ideal -- Flooding Flooding converges first No queuing delays SPIN-PP Reduces energy by 70% No redundant DATA messages

Limited Energy Simulations -- Ideal -- SPIN-EC -- SPIN-PP -- Flooding SPIN-EC distributes additional 20% data

Conclusions Successfully use meta-data negotiation to solve the implosion, overlap problem of simple flooding and gossiping. Resource-adaptive enhancements Simple scheme, small communication overhead, but a performance close to the ideal situation.

Future work Consider the cost of not only communicating data, but also synthesizing data, make it more realistic resource-adaptation protocols. Queuing delay, loss-prone nature of wireless channels can be incorporated and experimented.

Limitations The SPIN EC (Energy Constrained) version’s strategy may be too simple. There should be a topology dependant strategy, e.g. a narrow bridge connecting two connected component should be more energy conservative. The ideal criteria used to compare with SPIN is ideal in terms of data dissemination rate, so really not ‘ideal’ anymore when energy or other resources are limited, need a new goal function.

A Scalable and Robust Communication Paradigm for Sensor Networks 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” Tell me in what direction that vehicle in region Y is moving? As we repeatedly emphasize that diffusion is application-aware, before we go on illustrating the concept of diffusion, we need to come up with an example application first. And our example is remote surveillance application. In this application, users are interested to know animal locations in a specific region. As long as the users are still interested in such information, the report will be sent to them every t seconds.

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 Naming Data is named using attribute-value pairs Interests A node requests data by sending interests for named data Gradients Gradients is set up within the network designed to “draw” events, i.e. data matching the interest. Reinforcement Sink reinforces particular neighbors to draw higher quality ( higher data rate) events

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: Node data Type =four-legged animal Instance = elephant Location = [125, 220] Confidence = 0.85 Time = 02:10:35 Reply Request Interest ( Task ) Description Type = four-legged animal Interval = 20 ms Duration = 1 minute Location = [-100, -100; 200, 400]

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

Setting Up Gradient Interest = Interrogation Source Neighbor’s choices : 1. Flooding 2. Geographic routing 3. Cache data to direct interests Sink - Introduce sink and source - The user requests for low-data-rate events - Interest is sent and propagated. (the black arrow) - Gradients are set up. (the blue arrow) - Events are sent along the gradients. (the red arrow) - The thin red arrow show a low data rate. - The user reinforces the best path in terms of delay (the green arrow) - The thick red arrow shows a high data rate. - Suppose the middle node fail. - There is no high-data-rate events received. - The sink reinforces a low-data-rate path to recover from the node failure. Interest = Interrogation Gradient = Who is interested (data rate , duration, direction)

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

Reinforcing the Best Path Source 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 Sink - Introduce sink and source - The user requests for low-data-rate events - Interest is sent and propagated. (the black arrow) - Gradients are set up. (the blue arrow) - Events are sent along the gradients. (the red arrow) - The thin red arrow show a low data rate. - The user reinforces the best path in terms of delay (the green arrow) - The thick red arrow shows a high data rate. - Suppose the middle node fail. - There is no high-data-rate events received. - The sink reinforces a low-data-rate path to recover from the node failure. Low rate event Reinforcement = Increased interest

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. However, what we show you is just an example of local rules that can be used. There are several choices of local rules to use. Each of them lead to a different global behavior. In our example, we use flooding for interest propagation. But there are several sophisticated rules that can be used. For example, interest propagation that is based on cached information or GPS. There are also several choices of gradients. For our example, we use data-rate gradients. But probabilistic gradients are also possible. We may also deliver data using only a single path or multiple paths. Deterministically or probabilistically, Redundant or distinct, and so on. Our reinforcement is based on observed delay. We can also reinforce based on observed losses, delay variances, or energy level.

Local Behavior Choices For data transmission Multi-path delivery with selective quality along different paths probabilistic forwarding single-path delivery, etc. For reinforcement 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 Compare diffusion to flooding centrally computed tree (omniscient multicast) To evaluate diffusion, we compare with 2 idealized schemes. Flooding and omniscient multicast. For omniscient multicast, the multicast tree is centrally computed from the simulator. No overhead is assigned. We use 3 metrics for our evaluation To show energy efficiency, we measure the average dissipated energy per distinct event received. We also measure the delay so that we can imply the temporal accuracy of information. The event delivery ratio is measure to compare their robustness

Diffusion Simulation Details Simulator: ns-2 Network Size: 50-250 Nodes Transmission Range: 40m Constant Density: 1.95x10-3 nodes/m2 (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, and 35 mw in idle We evaluated our diffusion on several network size ranging from 50 to 250 nodes with constant density. We use IEEE 802.11 as our MAC but we are not quite happy with the choice at all. The main reason is that the energy consumption during idle intervals is too much, comparable to energy consumption in reception. So, we have modify the energy model such that it mimics a realistic sensor radio. In this energy model, the energy consumption during idle intervals is only 10% of energy consumption 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 All sources send the same location estimate for base experiments Here are parameters we used in our simulation. There are 5 sources and 5 sinks. Event size is very small. In our simulated scenario, all sources will detect the same animal so they send the same location estimates.

Average Dissipated Energy 0.018 0.016 Flooding 0.014 0.012 0.01 Average Dissipated Energy (Joules/Node/Received Event) 0.008 Omniscient Multicast 0.006 Diffusion 0.004 0.002 - The X axis is network size - The Y axis is dissipated energy - As expected, flooding performed the worst. - Diffusion can outperform omniscient multicast because they can perform application-level duplicate suppression while the other can’t. 50 100 150 200 250 300 Network Size Diffusion can outperform flooding and even omniscient multicast. (suppress duplicate location estimates)

Conclusions Can leverage data processing/aggregation inside the network Achieve desired global behavior through localized interactions Empirically adapt to observed environment The main challenges on the design of our data dissemination system are dynamics. There are several types of dynamics that our system need to deal with. For example, nodes may fail or move out of range. Deploying such sensor networks in a jungle, animals may walk by, become obstacles, play with the devices, and completely change the network topology. And even worse, after deploy networks for a while, you may need to re-task the network to do something else. It will be a pain to manually re-configure the huge number of devices for the new task. So, it is very important that our mechanism can adapt automatically to various types of dynamics.

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

Comparison of routing algorithms Attributes Algo. Data Efficiency Energy Efficiency (data/energy ratio) State complexity Flooding Fastest Low b/c Implosion Small, upstream Gossiping Slowest No. 7 Lowest Random walk None Rumor Routing Very slow No. 6 Very low Some SPIN Very Fast Higher than above, SPIN-EC close to ideal Data- neighbor pairs Directed Diffusion Quite Fast No. 3 Higher than TTDD global flooding + strong aggregation Complex: Neighbor X Interest TTDD No.2 Reasonable local flooding+ reasonable aggregation OK: Four neighbor, Constant IP Multicast Low: b/c heavy machinery, ‘big’ node Most complex

Thank you!