Sensor Networks

• Issues

• Solutions

• Some slides are from Estrin’s early talks

Disaster ResponseCirculatory Net

EmbedEmbed numerous distributed devices to monitor and interact with physical world: work-spaces, hospitals, homes, vehicles, and “the environment”

Network these devices so that they can coordinate to perform higher-level tasks.

Requires robust distributed systems of hundreds or thousands of devices.

Scenario

Motivating Applications

2 meters

Algae

-scaledTetheredRobot

Bio-Tank

Laboratory

Model Development

Inner wall of storm drain

Sensors

Environmental Monitoring

Sensors

Complex Structures

What is new?

• Tight coupling to the physical world– Need better physical models

– More experimentation

• Constraints of a sensor• Energy• Computing, communication, memory

• Failure and dynamics• Node failures, wireless communication

• Network scale• Most sensors are not mobile typically

Design Goals• Long-lived systems that can be untethered and unattended

– Low-duty cycle operation with bounded latency– Exploit redundancy and heterogeneous tiered systems

• Leverage data processing inside the network– Thousands or millions of operations per second can be done using

energy of sending a bit over 10 or 100 meters (Pottie00)– Exploit computation near data to reduce communication

• Self configuring systems that can be deployed ad hoc– Un-modeled physical world dynamics makes systems appear ad hoc– Measure and adapt to unpredictable environment– Exploit spatial diversity and density of sensor/actuator nodes

• Achieve desired global behavior with adaptive localized algorithms– Cant afford to extract dynamic state information needed for centralized

control

Sample Layered Architecture

Resource constraints call for more tightly integrated layers

Open Question:

Can we define anInternet-like architecture for such application-specific systems??

In-network: Application processing, Data aggregation, Query processing

Adaptive topology, Geo-Routing

MAC, Time, Location

Phy: comm, sensing, actuation, SP

User Queries, External Database

Data dissemination, storage, caching

Directed Diffusion

• In-network data processing (e.g., aggregation, caching)

• Application-aware communication primitives– expressed in terms of named data (not in terms of the

nodes generating or requesting data)

• Distributed algorithms using localized interactions and measurement based adaptation

Basic Directed DiffusionSetting up gradients

Source

Sink

Interest = Interrogation in terms of data attributes

Gradient = direction and strength

Basic Directed Diffusion

Source

Sink

Sending data and Reinforcing the “best” path

Low rate event Reinforcement = Increased interest

Directed Diffusion and Dynamics

Recoveringfrom node failure

Source

Sink

Low rate event

High rate eventReinforcement

Directed Diffusion and Dynamics

Source

Sink

Stable path

Low rate event

High rate event

Local Behavior Choices

• For propagating interests– In our example, floodIn our example, flood

– More sophisticated behaviors possible: e.g. based on cached information, GPS

• For data transmission– Multi-path delivery with Multi-path delivery with

selective quality along selective quality along different pathsdifferent paths

– probabilistic forwarding

– single-path delivery, etc.

• For setting up gradients• data-rate gradients are set data-rate gradients are set

up towards neighbors who up towards neighbors who send an interestsend an interest..

• Others possible: probabilistic gradients, energy gradients, etc.

• For reinforcement• reinforce paths, or parts reinforce paths, or parts

thereof, based on observed thereof, based on observed delaysdelays, losses, variances etc.

• other variants: inhibit certain paths because resource levels are low

Summary of Diffusion Results

• Under the investigated scenarios, diffusion outperformed omniscient multicast and flooding

• Application-level data dissemination has the potential to improve energy efficiency significantly– Duplicate suppression is only one simple example out of

many possible ways. – Aggregation (in progress)

• All layers have to be carefully designed– Not only network layer but also MAC and application lev

el

GRAB Design

• Two protocols addressing the two problems– Robust data delivery: MESH

• Deliver data to the user in face of node failures and packet losses

– Long-lived system: PEAS• Extend sensing and data delivery lifetime in proportion to

the total number of deployed nodes

Design Goal: a forwarding mesh with controllable width

• Forward each data packet along parallel paths to the sink

• these paths interleave to form a forwarding mesh

• The mesh starts at the source, ends at the sink

• The width of the mesh should be adjusted to achieve certain delivery reliability

source

sink

How to forward data along an adjustable mesh

• build a cost field that gives each sensor the direction towards the sink

• assign each packet certain amount of credit which controls the width of the forwarding mesh

How to build a cost field?

• The sink broadcasts an ADV packet with cost 0

• Each node sets its cost as the smaller of– Its own cost ( initially)– The sum of the cost of the sender and the link

cost to the sender

• Then broadcasts its own cost

Excessive messages in building the cost field

Sink(0)

B

C

4

1.5

1

sink broadcasts

B (1)

C (4)

C, B broadcasts

C (2.5)

C broadcasts again• the farther a node, the more it broadcasts• an example: 1500 nodes, 150mx150m field, the farthest node broadcasts more than 150 times, each node broadcasts 50 times on average

A node waits for a time proportional to its cost

Sink (0)

B

C

4

1.5

1

T=0, sink broadcasts. B and C set timers, expiring after 1, 4 seconds

B (1)

C (4)

T=1, B broadcasts, C cancels the first timer andsets another one that expires after 1.5 seconds

B

C (2.5)

T=2.5, C broadcasts when its timer expires

How to control the width of the mesh

• Each packet carries a credit

• A copy can take any path that requires a cost <= credit + Cost_source

• Different copies can take different paths, forming a mesh

sink

source

Cost <= credit + Cost_source

Cost > credit + Cost_source

Cost_source

Allocate credit along different hops• Calculate how much

credit has been used:– alpha_used =

P_consumed + C_A – C_source

• Calculate how much is remaining

– R_alpha = (alpha – alpha_used) / alpha

• Compare to a threshold– R_thresh = (C_A /

C_source)^2

sink

Cost_source

cost_consumed

source

A

Cost_A

Handling mobility

Source

Stimulus

Sink

Sink

Mobile SinkExcessive PowerConsumption

Increased WirelessTransmissionCollisions

State MaintenanceOverhead

Challenges• Battery powered sensor nodes• Communication via wireless links

– Bandwidth constraint– Load balancing

• Ad-hoc deployment in large scale– Fully distributed w/o global knowledge– Large numbers of sources and sinks

• Unexpected sensor node failures• Sink mobility

– No a-priori knowledge of sink movement

Goal, Idea

• Efficient and scalable data dissemination from multiple sources to multiple, mobile sinks

• Two-tier forwarding model– Source proactively builds a grid structure– Localize impact of sink mobility on data

forwarding– A small set of sensor node maintains

forwarding state

TTDD Basics

Source

Dissemination Node

Sink

Data Announcement

Query

Data

Immediate DisseminationNode

TTDD Mobile Sinks

Source

Dissemination Node

Sink

Data Announcement

Data

Immediate DisseminationNode

Immediate DisseminationNode

TrajectoryForwarding

TrajectoryForwarding

TTDD Multiple Mobile Sinks

Source

Dissemination Node

Data Announcement

Data

Immediate DisseminationNode

TrajectoryForwarding

Source

Other layers

• MAC layer– Energy efficiency and simplicity

• Time synchronization

• Location service

• Security

• transport