my tutorial on sensordb design issues at sigmod 2007

System Design Issues In Sensor Databases

Qiong Luo and Hejun Wu

Department of Computer Science and Engineering

The Hong Kong University of Science & Technology

http://www.cse.ust.hk/

SIGMOD07 Tutorial SensorDB System Design Issues 2

Wireless Sensor Networks (WSNs)

Energy efficiency is the most crucial performance factor.

Limited on-node resource Multi-hop communication


In-Network Sensor Query Processing (Sensor Databases, SensorDBs)

sink

SELECT temperature

FROM sensors

WHERE temperature > 900

SAMPLE INTERVAL 60s

σ,π,αSchedulerSensing & Networkingσ,π,α

SchedulerSensing & Networking

σ,π,αSchedulerSensing & Networking

σ,π,αSchedulerNetworking

SELECT avg (light)

FROM sensors

SAMPLE INTERVAL 60s

light: 1000

light: 500

light: 300

Avg light: 300/1

Avg light:

1000/1

Avg light: (1000+500)/2


Two Representative SensorDBs

Cougar [BGS01, YG03] Model sensor network data as sequences Declarative query interface with UDFs Cross-layer optimization in later versions

TinyDB [MF+02, 03] Declarative query interface Efficient and extensible framework Open-source implementation on real nodes


Advantages of Sensor Databases

Flexibility Declarative SQL style queries Dynamic query injection and removal

Efficiency Cross-layer optimization

E.g., in-network filtering and aggregation


Challenges in Sensor Databases

Dynamic data streams Hardware resource limitations

Limited per-node computing power and storage Unreliable wireless communication Battery power supply

Complex, networked, embedded software Blurred boundaries between components Plenty of cross-layer optimization opportunities


Focus of this Tutorial

System design issues in sensor databases Software architecture Operating system support Media Access Control (MAC) Routing Scheduling

These issues often dominate the overall performance.


Outline Introduction WSN hardware

Computing, sensing, communication, and power supply Software architecture Operating system support MAC protocols Routing Scheduling Summary and future directions


Current Sensor Node Hardware: Computing and Storage

Low-power microcontroller (CPU of a node) E.g., Atmega128 (MICA series), MSP430 (Telos

series), ARM/THUMB (XYZ sensor), and the latest 180MHz ARM920 (SunSpot).

Limited memory RAM

≤ 10KB SRAM (Static RAM) ROM

Usually ≤ 1MB flash memory


Current Sensor Node Hardware: Sensing and Radio

Sensing devices Electronic, mechanic, bio-chemical, …

Radio transceiver Fixed radio frequency Omni-direction radio signal Transmission rate ≤ 200 kbps Transmission range ≤ 50 meters


Current Sensor Node Hardware: Power Supply and Consumption

Power supply Batteries, usually <= 2000 mAh

Electric currents in a node Sleep 15-20 µA Radio on

Idle 20-25 mA, compute 25-30 mA Radio off

Idle 1-5 mA, compute 5-10 mA

Sleeping is the most effective means to save energy.


Outline Introduction WSN hardware Software architecture Operating system support MAC protocols Routing Scheduling Summary and future directions


Common Software Architecture of Sensor Databases

Scheduling

Operating System Kernel

Boundaries between components in a sensorDB are blurred.


Outline Introduction WSN hardware Common SensorDB software architecture Operating system support

Hardware management Application code development and deployment

MAC protocols Routing Scheduling Summary and future directions


TinyOS (http://www.tinyos.net/) De facto OS for sensor nodes

Early research effort Open source development Wide presence in commercial products Component-based architecture

Adaptive to hardware changes Lightweight for various applications

Event-driven processing Responsive to sensor signals and radio messages


TinyOS Application

Sensor devicesMote main board

Hardware manipulation componentsAbstraction:

Hardware:

Core system components

Lib components TinyOS interface components

Commands Events

Kernel:

Application

TinyOS startup (“Main”)Runable image of a TinyOS application

A TinyOS application is compiled with TinyOS components.


Some Limitations of TinyOS

Static code and memory No virtual memory No dynamic memory allocation No dynamic code update Task execution without priorities

Single thread

Global

Free

Stack

TinyOS memory allocation


Contiki [DGV04] Multi-threading Lightweight program

loading Lightweight

communication stacks uIP

A micro-version of RFC-compliant TCP/IP

Rime A lightweight

communication stack for low-power radio

core

#1 #2

#3

#n…

Multi-threading in Contiki


SOS [HK+05] Dynamic module

loading Allows incremental

update of binary code Runtime safety

mechanisms Memory monitoring Watchdog

Restart when system hangs

…Module #1

Module #2

Module #N

SOS Kernel

Jump table

1

2

System function call

Actual function to call


MANTIS [BC+05] Multi-threading Remote testing Scheduler for duty-

cycle sleeping Small code size

Uses less than 500B RAM and 14KB flash memory

Device driver Communication LayerKernel / Scheduler

Sensor Node Hardware

MANTIS System API

#1

User threads

#n…Network

StackCommand

Server


Load

t-kernel [GS06] OS protection

Separates OS/app space

Virtual memory Extends the limited SRAM

Preemptive scheduling Allows priorities

Fault tolerance Prevents system hang-up

from application errors

Application binary code

Naturalization

Run

Running an app. in t-kernel


On-Node Virtual Machines SunSPOT

A compact Java language

Java VM directly runs in on-node flash memory

SwissQM [MAK07] Combines a powerful

gateway with a virtual machine at the sensors

Query Machine (QM)

Bytecodeinterpreter

QM programs

Sensors

Operand stack

Query synopsis

Transmission buffer

http://www.sunspotworld.com/


Declarative Sensor Networks [CP+07] Snlog language

Datalog-like, declarative Suitable for polynomial-time

programs Useful in a variety of apps

Snlog compiler Translate Snlog into NesC

Runtime system Components of user provided

rules No on-node interpreter

Snlog program

nesC Templates

DSN runtime components Generated

NesC program

NesC Compiler

Snlog front-end

Execution planner

NesC backend

Snlog compiler

Binary code to be executed


Summary on OS Support

Support app. development and deployment Programming interfaces Code compilation and generation Runtime loading and modification

Provide hardware resource management Sensor signals, radio messages Memory allocation and virtualization Scheduling and system safety


OS and Sensor Databases

Desirable OS features for sensor databases Multiple applications Multi-threading Virtual memory Priority scheduling Reliability and fault tolerance


Outline Introduction WSN hardware Common SensorDB software architecture Operating system support MAC protocols

CSMA, STEM, S-MAC, and T-MAC Routing Scheduling Summary and future directions


CSMA (Carrier Sense Multiple Access)

Random delay before transmission attempt Node needs to keep idle listening before its

communication done Wireless collision remains a major problem

Reason: no effective coordination between nodes

Sender 1

Sender 2

transmitting

transmitting

Collision


Sparse Topology and Energy Management (STEM) [STS02]

Periodic wake up and listen Sleep when no packet to send and receive

Transmit

Sleep

Power

Listening Listening Listening

time


S-MAC (Sensor-MAC) [YHE02]

Schedules nodes to periodically sleep Coordinates the sleeping time of neighbors

for reliable transmission

Listen

SYNC RTS

Receiver

Sender

Sleep


T-MAC [DL03] Contention based protocol Dynamically ends an active period

Adapts to the needs for computation and communication

Aha, no more to do!, zzz~

S-MAC

T-MAC

Active time Sleep time

Sleep time

Active time

TA


Summary on MAC Important for performance

Communication quality Signal errors Noise

Communication energy Sleeping nodes Retransmission

Communication delay Negotiation for channels Wireless signal transmission delay

Significant to data quality, energy efficiency and response time in query processing!


MAC and Sensor Databases

MAC behavior of sensor databases Mostly converge-cast Periodic data flows

Opportunities of sensor databases for MAC Sleep scheduling that suits the data flows


Outline Introduction WSN hardware Common SensorDB software architecture Operating system support MAC protocols Routing

MintRoute, TinyAODV, and Directed Diffusion Scheduling Summary and future directions


Location-Based Routing

Requires location information Usually finds the shortest, reliable path using

location information of each node Energy aware

Suitable for queries with spatial predicates Can route queries to some specific regions

S

D

Transmission range of S

Shortest path


Flooding Every node broadcasts received data

Broadcast can be reduced by hop count Advantage

Simple Problems

Message implosion Many duplicates

Resource inefficiency Most nodes busy No sleeping


Directed Diffusion [IG+00]

Queries are defined as interests. Sink nodes post interests. Source nodes generate sensory data. Source nodes select reliable and efficient

routes to the sink nodes to forward data.


Illustration of Directed DiffusionSink node

Source node Source node

Sink node

Source node

Sink nodeStep 3: Reinforce one

path

Step 1: Sink nodepropagates Interest (query)

Step 2:Set up gradients


MintRoute [WTC03]

Implemented in TinyOS Can be used in TinyDB

More than simple shortest-path routing Monitors link connectivity Decides a route based on both link quality and

distance

1

23

1

23

Unreliable, high loss rate


TinyAODV (Tiny Ad-hoc On-Demand Distance Vector)

Builds paths only when needed Uses sequence number in RREQ to avoid

cycles

RREQ

RREQ: Route Request

RREP: Route Reply

RERR: Route ERRRREP

DATA

XRERR

…

Source Destination

…


Summary on Routing Focus of current routing protocols

Efficient forwarding Shortest path or least retransmission

Load balancing Avoid hot spots of heavy traffic

Open issues Reliability

Node failure Noise

Communication delay Find the path of the minimal delay


Routing and Sensor Databases

Routing characteristics of sensor databases Mainly converge-casting Not all nodes satisfy a query all the time.

Opportunities of sensorDBs for routing Data flow aware routing

Busy nodes get better routes. Busy queries get better routes.

Query type aware routing Aggregation, duplicate-sensitivity, join


Outline Introduction WSN hardware Common SensorDB software architecture Operating system support MAC protocols Routing Scheduling

FPS, Sichitiu’s Scheme, and DCS Summary and future directions


Goal of Scheduling

Communication efficiency and reliability Coordinate nodes in communication

Wireless collisions among neighbors No receiving on sleeping nodes

Sending… zzz… zzz…Done!

Data

Lost!


Centralized Scheduling

The base station specifies schedules for all nodes. The base station must be aware of the workload

and the network topology Hard to scale Hard to adapt to changes

…

Sink

Schedule


Distributed Scheduling Scheduling in TinyDB (query layer)

A node keeps active for 4 seconds and sleeps in the remaining time in a sample interval.

Sichitiu’s Scheduling Scheme [Sic04] Schedules at the MAC and routing layers Sets up both routing paths and schedules

Schedule construction is time consuming and unreliable because it needs the sink to confirm.


FPS (Flexible Power Scheduling) [HDB04]

Routing-layer distributed scheduling A parent node assigns transmission slots to

its children to avoid collision between siblings.

Collisions among non-sibling neighbors are possible.

sleep sleep compute transmit …Slots:

Time


DCS (Distributed Cross-Layer Scheduling) [WLX06]

Slot based Takes query processing cycles into account

Receiving, computing, transmission, and sleep Not only parents assign schedules to children, but

neighbors also negotiate. Able to avoid the collisions at the receiving nodes Attempts to assign consecutive transmission slots to

each node


DCS Components

Routing Layer

Query Layer

MAC Layer

Route Maintenance

Selection / Projection / Join / Aggregation

Transmission / Receiving

Route Selection

Collision Detection

Scheduling Module

Schedule Construction

Time Synchronization

Query Scheduling

Schedule E

xecution


Slot in a Schedule

A slot is a time period of fixed length. Transmission, Sleeping, PL/R (Processing,

Listening / Receiving), and Q/M (Query injection / route Maintenance)

Slot number s at t: The length of a slot is ls. The schedule start time is t0. A sample interval has m slots.

mmodls

tts

0


An Example Schedule

Time

Leaf

Le

af

S

ink

0

Transmission

PL/R

Q/MActive (sink only)

Sleeping2

Routing

tree

1

3

0

Node 1

Node2

Node3

Ho

p

0H

op

1

Ho

p

2H

op

2


Energy Efficiency

Measured in VMNet

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

2 10 60Sample Interval (s)

Po

wer

Co

nsu

mp

tion

(Jo

ule

s)

.

Original TinyDB Optimized TinyDB

Measured in the 10-Node Real WSN

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

2 10 60Sample Interval (s)

Po

wer

Co

nsu

mp

tion

(Jo

ule

s)

.

Original TinyDB Optimized TinyDB

DCS achieves 50-60% energy saving.


Summary on Scheduling

Scheduling is done on one or more layers. Scheduling is crucial for performance.

Communication reliability Coordination of nodes

Energy consumption Sleep scheduling

Response time Different transmission timings of neighboring nodes

result in different delays.


Scheduling and Sensor Databases

SensorDBs are complex to schedule. Opportunities in scheduling for sensorDBs

On-node multi-query scheduling Limited resources Changing sensor environments

Query-aware transmission scheduling Interaction between scheduling and query

execution


Outline Introduction WSN hardware Common SensorDB software architecture Operating system support MAC protocols Routing Scheduling Summary and future directions


Tutorial Summary System design issues have a significant

impact on the overall performance of sensor databases.

A holistic sensor database system requires considerations on all layers – from OS kernel, MAC, routing to query processing.

Cross-layer design is necessary, especially in scheduling.


Future Directions

Multi-query processing in sensor networks Query optimization

Sharing, caching, and pipelining Scheduling

Queries, operators, and transmission

In-network joins among different nodes Fine-grained scheduling for node cooperation

Query processing in multi-sink networksCross-layer design is necessary for an efficient, holistic sensor database.


References: Sensor Databases and Runtime Support

[BGS01] Philippe Bonnet, Johannes Gehrke, and Praveen Seshadri. Towards Sensor Database Systems. MDM, 2001 .

[MF+02] Samuel Madden, Michael J. Franklin, Joseph M. Hellerstein, and Wei Hong. TAG: a Tiny AGgregation Service for Ad-Hoc Sensor Networks. OSDI, 2002.

[MF+03] Samuel Madden, Michael J. Franklin, Joseph M. Hellerstein, and Wei Hong. The Design of an Acquisitional Query Processor for Sensor Networks. SIGMOD, 2003.

[YG03] Yong Yao and Johannes Gehrke. Query Processing for Sensor Networks. CIDR, 2003.

[MAK07] Rene Muller, Gustavo Alonso, and Donald Kossman. SwissQM: Next Generation Data Processing in Sensor Networks. CIDR, 2007.

[CP+07] David Chu, Lucian Popa, Arsalan Tavakoli, Joseph M. Hellerstein, Philip Levis, Scott Shenker, and Ion Stoica. The Design and

Implementation of A Declarative Sensor Network System. Submitted for publication, 2007.


References: OS Support[DGV04] Adam Dunkels, Björn Grönvall, and Thiemo Voigt. Contiki - A

Lightweight and Flexible Operating System for Tiny Networked Sensors. The 29th Annual IEEE Conference on Local Computer Networks, 2004.

[HK+05] Chih-Chieh Han, Ram Kumar, Roy Shea, Eddie Kohler and Mani Srivastava. A Dynamic Operating System for Sensor Nodes. International Conference on Mobile Systems, Applications, and Services, 2005.

[BC+05] Shah Bhatti, James Carlson, Hui Dai, Jing Deng, Jeff Rose, Anmol Sheth, Brian Shucker, Charles Gruenwald, Adam Torgerson, and Richard Han. MANTIS OS: An Embedded Multithreaded Operating System For Wireless Micro Sensor Platforms. ACM/Kluwer Mobile Networks and Applications (MONET), Special Issue on Wireless Sensor Networks, vol. 10, no. 4, pp.563–579, Aug 2005.

[GS06] Lin Gu and John A. Stankovic. t-kernel: Providing Reliable OS Support for Wireless Sensor Networks. SenSys, 2006.


References: MAC and Routing[STS02] Curt Schurgers, Vlasios Tsiatsis, and Mani B. Srivastava.

STEM: Topology Management for Energy Efficient Sensor Networks. IEEE Aerospace Conference, 2002.

[YHE02] Wei Ye, John Heidemann, and Deborah Estrin. An Energy-Efficient MAC Protocol for Wireless Sensor Networks. INFOCOM, 2002.

[DL03] Tijs van Dam and Koen Langendoen. An Adaptive Energy-Efficient MAC Protocol for Wireless Sensor Networks. SenSys, 2003.

[IGE00] Chalermek Intanagonwiwat, Ramesh Govindan, and Deborah Estrin. Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks. MobiCom, 2000.

[WTC03] Alec Woo, Ternence Tony, and David Culler. Taming the Underlying Challenges of Reliable Multihop Routing in Sensor Networks. SenSys, 2003.


References: Scheduling[HDB04] Barbara Hohlt, Lance Doherty, and Eric Brewer. Flexible

Power Scheduling for Sensor Networks. IPSN, 2004.

[Sic04] Mihail L. Sichitiu. Cross-Layer Scheduling for Power Efficiency in Wireless Sensor Networks. INFOCOM, 2004.

[WLX06] Hejun Wu, Qiong Luo, and Wenwei Xue. Distributed Cross-Layer Scheduling for In-Network Sensor Query Processing. PerCom, 2006.

my tutorial on sensordb design issues at sigmod 2007

Documents

current sensor node

avg light

node computing power

sensor databasesqiong

scheduler sensing networking

armthumb xyz sensor

s light

scheduler networking