Routing and Data Dissemination Presented by Li Huan

Routing and Data Dissemination Presented by: Li, Huan Liu, Junning UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Outline n n n Motivation and Challenges Basic Idea of Three Routing and Data Dissemination schemes in Sensor Networks Some Thoughts on Comparison of the Data dissemination schemes UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Differences with Current Networks n Difficult to pay special attention to any individual node: n n n Collecting information within the specified region Collaboration between neighbors Sensors may be inaccessible: n n embedded in physical structures. thrown into inhospitable terrain. UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Differences with Current Networks n Sensor networks deployed in very large ad hoc manner n n No static infrastructure They will suffer substantial changes as nodes fail: n n n battery exhaustion accidents new nodes are added. UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Differences with Current Networks n User and environmental demands also contribute to dynamics: n n n Nodes move Objects move Data-centric and application-centric n n Location aware Time aware UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Overall Design of Sensor Networks n One possible solution? n 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 § UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Why Different and Difficult? n A sensor node is not an identity (address) § Content based and data centric ü Where are nodes whose temperatures will exceed more than 10 degrees for next 10 minutes? ü Tell me the location of the object ( with interest specification) every 100 ms for 2 minutes. UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Why Different and Difficult? n n Multiple sensors collaborate to achieve one goal. Intermediate nodes can perform data aggregation and caching in addition to routing. ü where, when, how? UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Why Different and Difficult? n n Not node-to-node packet switching, but node-to-node data propagation. High level tasks are needed: ü ü At what speed and in what direction was that elephant traveling? Is it the time to order more inventory? UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Challenges n n Energy-limited nodes Computation n Aggregate data Suppress redundant routing information Communication n n Bandwidth-limited Energy-intensive Goal: Minimize energy dissipation UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Challenges n Scalability: ad-hoc deployment in large scale n n Fully distributed w/o global knowledge Large numbers of sources and sinks Robustness: unexpected sensor node failures Dynamically Change: no a-priori knowledge n n sink mobility target moving UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Challenges n n 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? n n n Small-chip based sensor nodes Large sensors, e. g. , rada Moving sensors, e. g. , robotics UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

SPIN: The Goal Broadcast with minimum energy W. R. Heinzelman, J. Kulik, H. Balakrishnan UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Conventional Approach A C B D E F n Flooding n n G Send to all neighbors E. g. , routing table updates UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Resource Inefficiencies Implosion n (a) A B (a) C (a) D n n Data overlap q r A (q, r) s B C (r, s) Resource blindness UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

What is the optimum protocol? A C B D E F n “Ideal” n n G Shortest-path routes Avoids overlap Minimum energy Need global topology information UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Two basic ideas n n 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 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

SPIN Family Sensor Protocol for Information via Negotiation n Data negotiation n n SPIN messages n n Meta-data (data naming) Application-level control Model “ideal” data paths ADV- advertise data REQ- request specific data DATA- requested data ADV A B REQ A B DATA A B Resource management UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

RE REQ DAADTV A Q V A ATD A D DATA Q TEA A R D Q V VEA DDRT A DAA A B AD V REQ D AD TA V ADV REQ SPIN-PP Example: UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

SPIN on Point-to-Point Networks n SPIN-PP n n 3 -stage handshake protocol Advantages n n n Simple Minimal start-up cost SPIN-EC n n SPIN-PP + low-energy threshold Modifies behavior based on current energy resources UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Test Network 25 Nodes 59 Edges Average degree = 4. 7 neighbors Network diameter = 8 hops 16 bytes Antenna reach = 10 meters Meta-Data 500 bytes Data UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Unlimited Energy Simulations -- SPIN-PP -- Ideal -- Flooding n Flooding converges first n n No queuing delays SPIN-PP n n Reduces energy by 70% No redundant DATA messages UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Limited Energy Simulations -- Ideal -- SPIN-EC -- SPIN-PP -- Flooding n SPIN-EC distributes additional 20% data UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

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. UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Future work n n Consider the cost of not only communicating data, but also synthesizing data, make it more realistic resourceadaptation protocols. Queuing delay, loss-prone nature of wireless channels can be incorporated and experimented. UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Limitations n The SPIN EC(Energy Constrained) version’s strategy may be too simple. n n 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. UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Directed Diffusion A Scalable and Robust Communication Paradigm for Sensor Networks C. Intanagonwiwat R. Govindan D. Estrin UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Application Example: Remote Surveillance n n 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? UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Basic Idea n n n In-network data processing (e. g. , aggregation, caching) Distributed algorithms using localized interactions Application-aware communication primitives n expressed in terms of named data UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Elements of Directed Diffusion n Naming n n Interests n n A node requests data by sending interests for named data Gradients n n Data is named using attribute-value pairs Gradients is set up within the network designed to “draw” events, i. e. data matching the interest. Reinforcement n Sink reinforces particular neighbors to draw higher quality ( higher data rate) events UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Naming n Content based naming n n n Tasks are named by a list of attribute – value pairs Task description specifies an interest for data matching the attributes Animal tracking: Request Interest ( Task ) Description Type = four-legged animal Interval = 20 ms Duration = 1 minute Location = [-100, -100; 200, 400] Reply Node data Type =four-legged animal Instance = elephant Location = [125, 220] Confidence = 0. 85 Time = 02: 10: 35 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Interest n n The sink periodically broadcasts interest messages to each of its neighbors Every node maintains an interest cache n n 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 n n Timestamp: last received matching interest Several gradients: data rate, duration, direction UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Setting Up Gradient Source Neighbor’s choices : 1. Flooding 2. Geographic routing 3. Cache data to direct interests Sink Interest = Interrogation Gradient = Who is interested (data rate , duration, direction) UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Data Propagation n n Sensor node computes the highest requested event rate among all its outgoing gradients When a node receives a data: n Find a matching interest entry in its cache n n n 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 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

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 Low rate event Sink Reinforcement = Increased interest UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

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. UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Local Behavior Choices n For data transmission n Multi-path delivery with selective quality along different paths probabilistic forwarding n single-path delivery, etc. n n For reinforcement n n reinforce paths based on observed delays losses, variances etc. UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Initial simulation study of diffusion n Key metric n Average Dissipated Energy per event delivered n n indicates energy efficiency and network lifetime Compare diffusion to n n flooding centrally computed tree (omniscient multicast) multicast UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Diffusion Simulation Details n n n Simulator: ns-2 Network Size: 50 -250 Nodes Transmission Range: 40 m Constant Density: 1. 95 x 10 -3 nodes/m 2 (9. 8 nodes in radius( MAC: Modified Contention-based MAC Energy Model: Mimic a realistic sensor radio [Pottie 2000[ n 660 m. W in transmission, 395 m. W in reception, and 35 mw in idle UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Diffusion Simulation n Surveillance application n n n 5 sources are randomly selected within a 70 m x 70 m 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 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Average Dissipated Energy (Joules/Node/Received Event) 0. 018 0. 016 Flooding 0. 014 0. 012 0. 01 0. 008 Omniscient Multicast 0. 006 0. 004 Diffusion 0. 002 0 0 50 100 150 200 250 300 Network Size Diffusion can outperform flooding and even omniscient multicast. (suppress duplicate location estimates) UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Conclusions Can leverage data processing/aggregation inside the network § Achieve desired global behavior through localized interactions § Empirically adapt to observed environment § UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Comments n n Primary concern is energy Simulations only n n n Only use five sources and five sinks How to exam scalability? ? ? ? UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

TTDD: A Two-tier Data Dissemination Model for Large-scale Wireless Sensor Networks Haiyun Luo Fan Ye, Jerry Cheng Songwu Lu, Lixia Zhang UCLA CS Dept. UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Assumptions n n Fixed source and sensor nodes, mobile or stationary sinks nodes densely applied in large field Position-aware nodes, sinks not necessarily Once a stimulus appears, sensors surrounding it collectively process signal, one becomes the source to generate the data report UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Sensor Network Model Sink Stimulus Source Sink UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Mobile Sink Excessive Power Consumption Increased Wireless Transmission Collisions State Maintenance Overhead UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Goal, Idea n n Efficient and scalable data dissemination from multiple sources to multiple, mobile sinks Two-tier forwarding model n n n Source proactively builds a grid structure Localize impact of sink mobility on data forwarding A small set of sensor node maintains forwarding state UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Grid setup n n n Source proactively divide the plane into αXα square cells, with itself at one of the crossing point of the grid. The source calculates the locations of its four neighboring dissemination points The source sends a data-announcement message to reach these neighbors using greedy geographical forwarding The node serving the point called dissemination node This continues… UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

TTDD Basics Dissemination Node Data Announcement Source Data Sink Query Immediate Dissemination Node UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

TTDD Mobile Sinks Dissemination Node Trajectory Forwarding Data Announcement Source Immediate Dissemination Node Data Sink Immediate Dissemination Node Trajectory Forwarding UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

TTDD Multiple Mobile Sinks Dissemination Node Trajectory Forwarding Data Announcement Source Data Immediate Dissemination Node Source UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Grid Maintenance n Issues: n n n Efficiency Handle unexpected dissemination node failures Solutions: n n n Source sets the Grid Lifetime in Data Announcement DN replication: each DN recruits several sensor nodes from its one-hop neighbor, replicates the location of the upstream DN DN failure detected and replaced on-demand by on-going query and data flows UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Grid Maintenance Dissemination Node Source X Data Immediate Dissemination Node UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Grid Maintenance (cont’d) Dissemination Node Source X Data Immediate Dissemination Node UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Ns-2 Simulation n Metrics n n Energy consumption, delay, success rate Impacts of n n Cell size Number of sources and sinks Sink mobility Node failure rates UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Conclusions n TTDD: two-tier data dissemination Model n n n First Infrastructure-approach in semistationary sensor networks n n Exploit sensor nodes being stationary and location-aware Construct & maintain a grid structure with low overhead Efficiency & effectiveness in supporting mobile sinks Proactive sources n Localize sink mobility impact UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Limitations and Future work n n n Knowledge of cell size Greedy geographical routing failures, it is not clear how the greedy geographical routing works in terms of the neighbor’s range, which may lead to a problem of finding two dissemination node for one Mobile stimulus Mobile sensor node Sink mobility speed: limited speed Data aggregation UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Comparison of routing algorithms Attributes 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 Very Fast No. 2 Reasonable local flooding+ reasonable aggregation OK: Four neighbor, Constant Fastest Low: b/c heavy machinery, ‘big’ node Most complex Algo. IP Multicast UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Discussions n Source initiated or Sink initiated? Why? n n n UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

Discussion (con) n Should we build more infrastructure or not, what’s the trade off? UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science

The End n Thank you! UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Computer Science