vivek kinra CS-WMU1 Overview of Directed Diffusion Professor: -Dr Ajay Gupta Presented By: -Vivek Kinra CS691 Spring2003

vivek kinra CS-WMU2 Note: -Various slides of this presentation are created with the help of presentation slides of UCLA,USC and various other sources

vivek kinra CS-WMU3 History Research started to investigate the design of localized algorithm using the Directed Diffusion model Research started to investigate the design of localized algorithm using the Directed Diffusion model The idea was developed in the context of a DARPA study by D.Estrin The idea was developed in the context of a DARPA study by D.Estrin Example of posing query for tanks/vehicles…….. Example of posing query for tanks/vehicles……..

vivek kinra CS-WMU4 Design Features Data centric: -Routing is based on data contained in sensor node and may not need ID Data centric: -Routing is based on data contained in sensor node and may not need ID Application focus on the data generated by sensors. Application focus on the data generated by sensors. Data is named by attributes and applications request data matching certain attribute values. Data is named by attributes and applications request data matching certain attribute values. Motivated by robustness, scaling and energy efficiency Motivated by robustness, scaling and energy efficiency

vivek kinra CS-WMU5 Directed Diffusion Developed by ISI/USC and UCLA is a novel network protocol built for info retrieval and data dissemination. Developed by ISI/USC and UCLA is a novel network protocol built for info retrieval and data dissemination. Data generated by nodes => attributes(A1) Data generated by nodes => attributes(A1) Sinks/nodes request data=>Interest into n/w Sinks/nodes request data=>Interest into n/w If A1 == Interest then(gradient setup in n/w) (Pedestrians) If A1 == Interest then(gradient setup in n/w) (Pedestrians)

vivek kinra CS-WMU6 contd Data pulled towards sinks =>receiver Initiated routing protocol Data pulled towards sinks =>receiver Initiated routing protocol Example target tracking Example target tracking Intermediate node might aggregate data Intermediate node might aggregate data Since all nodes in directed diffusion are application aware so It is completly application oriented. Since all nodes in directed diffusion are application aware so It is completly application oriented.

vivek kinra CS-WMU7 contd It is significantly different from IP style communication It is significantly different from IP style communication Not infeasible with IP or Ad-hoc routing Not infeasible with IP or Ad-hoc routing Imp Feature: - interest, data aggregation and propagation are determined by localized interaction Imp Feature: - interest, data aggregation and propagation are determined by localized interaction

vivek kinra CS-WMU8 Expected Architecture of Sensor Network Required capabilities of sensor node: - Required capabilities of sensor node: - A Match box sized form factor A Match box sized form factor Battery power source Battery power source Power conserving processor clocked at several hundred Mhz Power conserving processor clocked at several hundred Mhz Memory Memory Radio modem Radio modem

vivek kinra CS-WMU9 contd Energy efficient MAC layer Energy efficient MAC layer Can have more than 1 or more sensors e.g seismic geophones, infrared dipoles etc Can have more than 1 or more sensors e.g seismic geophones, infrared dipoles etc The Atod conversion on such system produce 70ksamples/sec and 12 bit resolution The Atod conversion on such system produce 70ksamples/sec and 12 bit resolution

vivek kinra CS-WMU10 For power issue, common signal processing functions offloaded to low power ASIC For power issue, common signal processing functions offloaded to low power ASIC Processor woke up only when event of Interest Processor woke up only when event of Interest A Sensor Node have a GPS receiver A Sensor Node have a GPS receiver The adv. Of these sensors is with very cheap in cost they obtain high SNR (attenuate with distance). The adv. Of these sensors is with very cheap in cost they obtain high SNR (attenuate with distance). Also can be deployed in huge amount Also can be deployed in huge amount

vivek kinra CS-WMU11 Energy concern Sensors Deployment falls in two ways: - Sensors Deployment falls in two ways: - Large complex system deployed far. Large complex system deployed far. Short range hop-hop communication is preferred over direct long range. Short range hop-hop communication is preferred over direct long range. Local computation to reduce data before transmission Local computation to reduce data before transmission

vivek kinra CS-WMU12 Contd In this organization, individual nodes reduce the sampled waveform generated by target (tank etc) into a relatively coarse grained “event” description. In this organization, individual nodes reduce the sampled waveform generated by target (tank etc) into a relatively coarse grained “event” description. Description =>”codebook value” (event code) Description =>”codebook value” (event code) Code->a timestamp,…… Code->a timestamp,…… Nodes exchanged this event code Nodes exchanged this event code

vivek kinra CS-WMU13 Method description Task conveyed to sensor N/W Task conveyed to sensor N/W Nodes tasks it’s sensors Nodes tasks it’s sensors Matches sampled wave form against locally stored library Matches sampled wave form against locally stored library Sensors in region may coordinate to pick best estimate. Sensors in region may coordinate to pick best estimate. Packet:-Attributes (type, amplitude, Intensity, region, time stamp……) Packet:-Attributes (type, amplitude, Intensity, region, time stamp……)

vivek kinra CS-WMU14 Naming Given Set of Tasks supported by sensor network selecting a naming scheme is first step in designing sensor networks. Given Set of Tasks supported by sensor network selecting a naming scheme is first step in designing sensor networks. Basically list of attribute value pairs. Basically list of attribute value pairs. E.g. For tracking animal its attributes should describe tasks like, type of animal, E.g. For tracking animal its attributes should describe tasks like, type of animal, geographic location to track, interval for sending updates, duration for which it was recorded (event occurrence time) geographic location to track, interval for sending updates, duration for which it was recorded (event occurrence time)

vivek kinra CS-WMU15 Data sent in response to Interest Type = four legged animal Type = four legged animal Instance = rabbit//instance of type Instance = rabbit//instance of type location = [125,220]/node location location = [125,220]/node location Intensity = 0.6/signal amplitude Intensity = 0.6/signal amplitude Confidence = 0.85//confi.. in match Confidence = 0.85//confi.. in match Timestamp = 01:20:40//event generation time Timestamp = 01:20:40//event generation time

vivek kinra CS-WMU16 Sink periodically broadcasts an interest message to each of its neighbors. Sink periodically broadcasts an interest message to each of its neighbors. Initial interest specifies a low data rate (e.g 1 event/sec) Initial interest specifies a low data rate (e.g 1 event/sec) Interest are diff based on type, rect or interval Interest are diff based on type, rect or interval Every node maintains a interest cache. Every node maintains a interest cache. Interest entries in cache do not contain info about sink Interest entries in cache do not contain info about sink

vivek kinra CS-WMU17 Interest entry Time stamp (last received matching) Time stamp (last received matching) Gradient field (up to 1/neighbor) Gradient field (up to 1/neighbor) G.F => data rate field (requested by neighbor)=>interval attribute G.F => data rate field (requested by neighbor)=>interval attribute Duration=timestamp – expiresAT Duration=timestamp – expiresAT No Entry No Entry No gradient No gradient

vivek kinra CS-WMU18 Event Sink Have u seen any four leg animal??? QUERY DIFFUSED IN TO INTEREST WHICH IS LIST OF ATTRIBUTE VALUE PAIRS Interest Propagation (Flooding) interests

vivek kinra CS-WMU19 YES I HAVE SEEN ONE …. INTIAL GRADIENTS SETUP(VALUE+DIRECTION) Two-way Gradient setup

vivek kinra CS-WMU20 Gradient setup/reinforced path Sink/Interest source I-Propagation Initial grad.. setup Data …..reinforced path

vivek kinra CS-WMU21 Interest/gradient Task ={type,rect,a duration of 10 min}is instantiated at particular node Task ={type,rect,a duration of 10 min}is instantiated at particular node Interval :- event data rate Interval :- event data rate Sink periodically broadcast interest msg (& refresh interest) to neighbors. Sink periodically broadcast interest msg (& refresh interest) to neighbors. Initial Interest :-{rect,duration attributes,larger interval attribute} Initial Interest :-{rect,duration attributes,larger interval attribute} Gradient expiration Gradient expiration

vivek kinra CS-WMU22 DATA DELIVERY THROUGH REINFORCED PATH SINGLE PATH DELIVERY (CAN BE MULTIPATH ALSO)

vivek kinra CS-WMU23 IN CASE OF NODE FAILURE USE ALTERNATIVE PATHS

vivek kinra CS-WMU24 Reinforcement When to reinforce ?(quality/delay matrices can be chosen) When to reinforce ?(quality/delay matrices can be chosen) Whom to reinforce ? Whom to reinforce ? How many to reinforce? How many to reinforce? When to send negative reinforcement When to send negative reinforcement

vivek kinra CS-WMU25 When?? Sink initially diffuses a interest for a low event-rate. Sink initially diffuses a interest for a low event-rate. Once sources starts detect a matching target they send low rate events. Once sources starts detect a matching target they send low rate events. After the sink starts receiving these low data rate events it reinforces one particular neighbor to draw down higher quality. After the sink starts receiving these low data rate events it reinforces one particular neighbor to draw down higher quality.

vivek kinra CS-WMU26 Whom?? To reinforce this neighbor, the sink re- sends the original interest message but with smaller interval (higher data rate). To reinforce this neighbor, the sink re- sends the original interest message but with smaller interval (higher data rate). Two approaches for reinforce Two approaches for reinforce Incremental approach:- Add min # of links to existing tree Incremental approach:- Add min # of links to existing tree Select links so that min energy is used Select links so that min energy is used

vivek kinra CS-WMU27 How Many Node must reinforce at least one neighbor Node must reinforce at least one neighbor

vivek kinra CS-WMU28 Negative Reinforcement Earlier used A but now B is better Earlier used A but now B is better One way :- time out all high data gradients in the n/w One way :- time out all high data gradients in the n/w Sink would periodically reinforce B and cease A that will degrade the path to A to lower data rate Sink would periodically reinforce B and cease A that will degrade the path to A to lower data rate Other way-:Degrade the path to A by re- sending the interest with low data rate Other way-:Degrade the path to A by re- sending the interest with low data rate

vivek kinra CS-WMU29 Whether to negatively reinforce or not N.R those neighbor from which no new event have been received. N.R those neighbor from which no new event have been received. Or few events are coming. Or few events are coming. Significant experiments are required before deciding which local rule achieve an energy efficient global behaviour Significant experiments are required before deciding which local rule achieve an energy efficient global behaviour

vivek kinra CS-WMU30 Issues of Concern Ad hoc, self organizing, adaptive systems with predictable behavior Ad hoc, self organizing, adaptive systems with predictable behavior Collaborative processing, data fusion, multiple sensory modalities Collaborative processing, data fusion, multiple sensory modalities Data analysis/mining Data analysis/mining

vivek kinra CS-WMU31 Issues yet to be resolved How to handle congested network? How to handle congested network? Semantics for gradients. Semantics for gradients. Handling of more than one sources. Handling of more than one sources. Negative reinforcement increases delay and contention Negative reinforcement increases delay and contention

vivek kinra CS-WMU32 comments (battery life, size, processing power, memory, etc.)? The paper presents a motion-detection scenario for sensor networks. (battery life, size, processing power, memory, etc.)? The paper presents a motion-detection scenario for sensor networks. To identify an event sources must match sampled sensor waveforms against signatures stored in a local library. To identify an event sources must match sampled sensor waveforms against signatures stored in a local library. To be useful, this library may have to store several thousand such signatures or more. To be useful, this library may have to store several thousand such signatures or more. We could implement "task-centric" sensor networks, where sensor nodes are focused on one or two type of event detection. We could implement "task-centric" sensor networks, where sensor nodes are focused on one or two type of event detection.

vivek kinra CS-WMU33 Tiny Diffusion Implementation of Diffusion on resource constrained USB motes Implementation of Diffusion on resource constrained USB motes 8 bit CPU, 8k program memory, 512 bytes data memory 8 bit CPU, 8k program memory, 512 bytes data memory Subsets of full system Subsets of full system Retains only gradients and condenses attributes to a single tag Retains only gradients and condenses attributes to a single tag Entire system runs for less than 5.5 KB memory Entire system runs for less than 5.5 KB memory

vivek kinra CS-WMU34 contd Tiny OS adds ~3.5 KB and 144 bytes of data (inclusive support for radio and photo sensor Tiny OS adds ~3.5 KB and 144 bytes of data (inclusive support for radio and photo sensor Diffusion adds ~2k code and 110 bytes of data to tiny OS Diffusion adds ~2k code and 110 bytes of data to tiny OS

vivek kinra CS-WMU35 Tiny Diffusion Functionality Resource Constraint Resource Constraint Limited Cache size-currently 10 entries of 2 bytes each Limited Cache size-currently 10 entries of 2 bytes each Limited ability to support multiple traffic stream. currently support 5 concurrently active gradients Limited ability to support multiple traffic stream. currently support 5 concurrently active gradients

vivek kinra CS-WMU36 TinyOS Implementation

vivek kinra CS-WMU37

vivek kinra CS-WMU38 Gateway Architecture TINYOS Tiny Diffusion Photo Data Source Data Sink Device Driver LINUX DIFFUSION Query Data Sink Acoustic Data Source TINYOS Transceiver RFM MOTE ATMEL MHz MCU 8K program memory 512 Bytes Data Memory RFM Radio 900 MHz PC104 AMD Elan™SC400 66MHz CPU 16MB RAM Form Factor: 3.6" x 3.8" x 0.6"

vivek kinra CS-WMU39 Tiered Testbed PC-104+(linux) with MoteNIC PC-104+(linux) with MoteNIC Tags, Sensor Card Tags, Sensor Card UCB Motes w/TinyOS UCB Motes w/TinyOS Yet to come: SmartDust (highly specialized nodes) Yet to come: SmartDust (highly specialized nodes) PS104 TAG USB Mote

vivek kinra CS-WMU40