INT598 Data-Centric Routing Protocols Silvia Nittel Spatial Information Science & Engineering University of Maine Fall 2006

INT598Data-Centric Routing

Protocols

Silvia NittelSpatial Information Science &

EngineeringUniversity of Maine

Fall 2006

INT598: Sensor System Foundation 2© Dr. Silvia Nittel, NCGIA, University of Maine, 2006

Overview Motivation & Applications Platforms, Operating Systems, Power Networking

Physical layer, MAC, Protocols Routing

Data-centric Routing Routing Protocols

Adaptable, Configurable Systems Data Collection and Aggregation


Data-centric Routing Named-data as a way of tasking motes, expressing data

transport request (data-centric routing) Basically:

“send the request to sensors that can deliver the data, I do not care about their address”

Two initial approaches in literature: Derived from multicast-routing perspective

where you name a logical group of sensor nodes (Diffusion)

Derived from database query language (TinyDB) with stronger semantics on data delivery, timing, sequencing

Commonality is tree-based routing Query sent out from microserver to motes Sink-Tree built to carry data from motes to

microserver


Tree Routing

A

B C

D

FE

Query

Parent Node

Children Nodes


Tree building Queries/Request

How to specify a query/request? To which nodes does a query go?

Neighbor selection How does a mote select upstream neighbor for data? Asymmetric links Unidirectional links Route characterization (like ETX)

Multiple microservers What about multiple microservers? How does mote select a microserver?


Tree building Dynamics

How often do you send out a new query? How often do you select a new upstream path

Design Tree building protocol From query source to data producer(s) and back Multihop ad-hoc routing

reliable routing is essential!


Basic Primitives Single-hop packet loss characteristics

Environment, distance, transmit power, temporal correlation, data rate, packet size

Services for high level protocols/applications Link estimation Neighborhood management Reliable multihop routing for data collection


Reliable Routing 3 core components for reliable routing

Neighbor table management (‘best neighbors’?)

Link estimation (of communication quality to neighbors)

Routing protocol


Routing in WiSeNets Communication between nodes in a

sensor network A) sending a request to a specific node

Node: at the right location, sensing the right data B) sending (sensed) data back to requesting

node(s) Objectives:

Achieve network connectivity (all nodes are reachable)

Find/reach sensor nodes that can contribute data

Establish ‘communication paths’


Routing Protocols for WiSeNets

Flooding Gradient Clustering and Cellular Geographic Energy-aware


Flooding SPIN – Sensor Protocol for Information

via Negotiation [HKB99], [HKB02]Heinzelman, Kulik, Balakrishnan

Flooding Classical flooding:

disseminate all observations to all nodes in the network

SPIN: Flooding variant See paper

Reliable, robust communication, but very expensive energy-wise


SPIN





Gradient-based Approach Influential approaches:

Directed Diffusion [IGE00]Intanagonwiwat, Govindan Estrin

GEAR – Geographical and Energy-Aware Routing [YGE01] Yu, Govindan, Estrin


Directed Diffusion Data-centric communication

All communication using named data Sets of attribute/value pairs are used to identify data Established gradients in the network are matched with

data to determine the next hop along route to sink Sensor nodes are task-aware

Sensor nodes respond to user specified interests (task descriptions or queries) matching their particular local capabilities (attached sensors)

Task description: specification of which data do to collect, in which sampling intervals

Local gradients are set up through interest propagation from sink to source (establishing path back to sink) (example!)

Path reinforcement to identify best route between nodes Data cached at intermediate nodes for aggregation and loop

prevention


Directed Diffusion

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Interest: type = 4 legged animal interval = 1 s rect = [0, 75, 25, 100] timestamp = 1:20:40 expires = 1:30:40

Interest TransmissionEstablished Gradient

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DataInterest TransmissionReinforced path

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Directed Diffusion

Multiple Sources Link FailureMultiple Sinks


Directed Diffusion Advantages:

Energy: much less traffic than flooding & data aggregation

Latency: usually transmitting data along best path Scalability: local interactions only Robust: retransmission of interests and low data rate

gradients

Disadvantages: Gradient setup phase expensive Retransmission of interests and alternate path

maintenance required*

Not energy aware – all messages traverse the primary path





Clustering Approach LEACH – Low Energy Adaptive Clustering Hierarchy

[HCB00], [HCB02] Heinzelman, Chandrakasan, Balakrishnan

Cluster-based hierarchical approach Nodes elect themselves to be cluster heads at the

beginning of each round based on a probability function Localized coordination for set-up Cluster heads randomly rotated to increase network

lifetime Cluster membership adaptive

Members communicate with cluster head using TDMA MAC

Data aggregation at cluster heads Cluster heads communicate directly with user


LEACH

Round r Round r + 1

Cluster Head Rotation: Cluster head


LEACH Positive

Energy: balances energy usage among nodes and allows nodes to shut down radios

Latency: only two hops to user Straightforward: aggregate data at cluster head and

send to user Scalability: distributed hierarchical approach

Negative Cluster head failure a problem Cluster head selection questionable (LEACH-C) Assumes all nodes capable of long range

transmissions





GEAR – Geographic and Energy Aware Routing [YGE01]

Greedy geographic query routing technique Cost function based on destination location

and neighbor node energies used to determine next hop

Improvement over Directed Diffusion’s ‘interest flooding’ technique

Less partitioning than GPSR [KK00]

Greedy Perimeter Stateless Routing Restricted broadcast within sampling

region


Summary Routing Protocols Critical elements of a sensor network protocol

Energy efficient Energy usage distributed among nodes Robust – fault tolerant Scalable – distributed control & local interactions Small footprint Low latency

Questions How to avoid draining sensor nodes near sinks? How feasible is data aggregation beyond duplicate

suppression? Better way to categorize network models? Good way to evaluate and compare protocols? How effective are the protocols in the real world?


Collaborative Processing Data acquired at

single node do not have enough information for certain task (e.g. target localization/tracking), collaboration across nodes is required

Collaboration makes processing more robust to noise, interference

Collaboration can be optimized to involve no more nodes than necessary

Microphone network for tank tracking


Time and Location Each sensor node creates local observations. Each observations is ‘tagged’ with the

observation time and sensor location, otherwise the observation has little usefulness All weather station data has station location

(longitude, latitude) and observation time (YY:MM:DD:HH:MM) associated with it

Audio data for localization need to be more accurately time/location-stamped

Problems: Node localization and time synchronization between nodes


Time Synchronization Also crucial in other

contexts Ranging, tracking, security,

MAC, aggregation etc. Global time not always

needed New ideas

Local timescales Receiver-receiver sync Multihop time translation Post-facto sync

Mote implementation ~10 s single hop Error grows slowly over

hops

Sender Receiver

NIC

Physical Media

NIC

Propagation Time

Receiver

NICI saw itat t=4 I saw it

at t=5

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A4

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C

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6B

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D11

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Node Localization

Snd_travel_time = Range/Snd_velocity

RF_travel_time = Range/RF_velocity

Snd_arrival_time – RF_arrival_time = Snd_travel_time – RF_travel_time

receiver

RF

Sound

RF/Sound ranging system

RF & Sound signal are sent simultaneously, their arrival time are recorded by the receiver


Node Localization (II)

When range among many nodes is known, we can uniquely assign coordinates to each node relative to a reference point

(0,0)

(10,10)

(12,11)


Other topics Distributed Control and Signal

Processing Tracking Coverage and Security Privacy Emerging Standards (ZIGBEE)

Documents

INT598 Data-Centric Routing Protocols Silvia Nittel Spatial Information Science & Engineering University of Maine Fall 2006