computer networks ii advanced features (t-110.5111) · wireless sensor networks with mobile...

Computer Networks II

Advanced Features (T-110.5111)

Wireless Sensor Networks

Mario Di Francesco, PhD

Assistant Professor – DCS Research Group

For classroom use only, no unauthorized distribution

Wireless sensor networks:

an introduction

Network architecture

Wireless sensor nodes

Approaches to energy conservation

G. Anastasi, M. Conti, M. Di Francesco, A. Passarella, “Energy conservation in wireless sensor networks: A

survey”, Ad Hoc Networks, 7(3):537–568, May 2009 (http://dx.doi.org/10.1016/j.adhoc.2008.06.003)

http://dx.doi.org/10.1016/j.adhoc.2008.06.003

Computer networks II – Advanced topics

T-110.5111 – Wireless sensor networks (13.10.2014)

Mario Di Francesco

http://www.uta.edu/faculty/mariodf

Wireless sensor network

Architecture and components

Sensing field

Sensor

Node

Sink

(Base station)

Remote

user

Internet



Mario Di Francesco


Wireless sensor node


ADCSensors Radio

Memory

MCU

DC-DCBattery

Mobilizer Location Finding SystemPower Generator

Power Supply Subsystem Sensing Subsystem Processing SubsystemCommunication

Subsystem



Mario Di Francesco




ADCSensors Radio

Memory

MCU

DC-DCBattery



Subsystem

Data acquisition

from the environment



Mario Di Francesco




ADCSensors Radio

Memory

MCU

DC-DCBattery



Subsystem

Local data processing

and data storage



Mario Di Francesco




ADCSensors Radio

Memory

MCU

DC-DCBattery



Subsystem

Short range wireless communication

Radio is the most power hungry component



Mario Di Francesco




ADCSensors Radio

Memory

MCU

DC-DCBattery



Subsystem

Battery powered devices

Batteries cannot be changed nor recharged



Mario Di Francesco


Examples of sensor nodes: UCB Motes



Mario Di Francesco


Examples of sensors (i.e., transducers)

Name Producer Type Power

consumption

STCN75 STM Temperature 0.4 mW

ADXL330 Analog Devices Accel. (3 axis) 0.2 mW

SHTx Sensirion Temperature/humidity 3 mW

iMEMS Analog Devices Accel. (3 axis) 30 mW

2200 and 2600

series

GEMS Pressure 50 mW

CP18, VL18,

GM60, GLV30

VISOLUX Proximity 350 mW

FCS-GL1/2A4-

AP8X-H1141

TURCK Flow control 1250 mW



Mario Di Francesco


Telos node: board and integrated circuits



Mario Di Francesco



Breakdown of energy consumption

0

2

4

6

8

10

12

14

16

Po

we

r (m

W)

SENSORS CPU TX RX IDLE SLEEP

Sending 1 bit of information is equivalent to process

~1000 instructions from as for energy consumption

RADIO



Mario Di Francesco



Breakdown of energy consumption

0

2

4

6

8

10

12

14

16

Po

we

r (m

W)

SENSORS CPU TX RX IDLE SLEEP

The power consumption of the sensor (transducer)

is not always negligible

Wireless sensor networks

Application scenarios and goals

Data collection

– Long-term network lifetime

– Self organization

Dense networks

– Multi-hop routes

– Interference



Mario Di Francesco


Energy Conservation Schemes for WSNs

Duty Cycling Data-driven Mobility-based

Energy conservation in WSNs

Mostly targeted to the radio

and the sensing (data acquisition) subsystems



Mario Di Francesco


Taxonomy of approaches

based on duty cycling

Duty Cycling

Topology Control

Location-driven Connectivity-

driven

Power Management

Sleep/Wakeup Protocols

Low-Duty Cycle MAC Protocols



Mario Di Francesco


Taxonomy of

(general) sleep/wakeup protocols

Sleep/wakeup Protocols

On-demand Scheduled

Rendez-vous Asynchronous

On demand: low-power radios, radio-triggered wakeup

Scheduled rendez-vous: synchronized wakeup

Asynchronous: wakeup at any time



Mario Di Francesco


Taxonomy of

MAC protocols with a low duty cycle

Low-Duty Cycle MAC Protocols

Time Division Multiple Access

Contention-based Hybrid

Time Division Multiple Access: Bluetooth, TRAMA

Contention-based: IEEE 802.15.4, B-MAC, S-MAC, T-MAC

Hybrid: Z-MAC, Crankshaft



Mario Di Francesco


Channel access

based on long preambles

Low-power listening

– Exploit transmit mode as it consumes less than receive mode

– Use a duty cycle for further energy savings

– Implemented by B-MAC and derived solutions (e.g., X-MAC)

Preamble Msg



Mario Di Francesco


Taxonomy of data-driven approaches

Data-driven

Data Reduction

In-network Processing

Data Compression

Data Prediction

Energy-efficient Data Acquisition

Adaptive Sampling

Hierarchical Sampling

Model-based Sensing



Mario Di Francesco


Example of data prediction:

differential sending strategy

Only send messages if values differ more than

send skip skip skip skip skip

send

skip

send skip skip skip skip



Mario Di Francesco


Example of data prediction:

model-based strategy

Send the description (representation) of the signal

send all messages

build and send the model

no messages

signal differs from model,

start over



Mario Di Francesco


Example of hierarchical sampling:

triggered sensing in smart environments

Event-triggered image capture

– Fall detection algorithm running at an ordinary sensor

– Tiered architecture with a multimedia sensor node

Ordinary Sensor

(Sun SPOT)

Multimedia Sensor Prototype

Sun SPOT(gateway)

BeagleBoard

Logitech C905

IEEE 802.15.4


with mobile elements

Definition and taxonomy

Sparse wireless sensor networks

Discovery of mobile elements

M. Di Francesco, S. K. Das, G. Anastasi, “Data Collection in Wireless Sensor Networks with Mobile Elements: A Survey”, ACM Transactions on Sensor Networks, 8(1):7, August 2011 (http://dx.doi.org/10.1145/1993042.1993049)

http://dx.doi.org/10.1145/1993042.1993049



Mario Di Francesco


WSNs with Mobile Elements

Main components

– (Regular) sensor nodes

Perform sensing as their main task

Sources of data

– Sinks (base stations)

Collect messages and use them or make them available

Destination of data

– Support nodes

Special nodes performing a specific task

They exploit mobility to support network operation

A network where at least one of them is mobile



Mario Di Francesco


Mobile Data Collectors

Mobile elements that visit the network

to gather data from source nodes

Classification

– Mobile sinks

Both dense and sparse WSNs

– Mobile relays

Support nodes that provide a relay (forwarding)

service between source nodes and the sink

Gather data from sensors, store them

and carry them to the base station

(Rather) sparse WSNs



Mario Di Francesco


Mobile sinks

Mobile

Sink

Mobile

Sink



Mario Di Francesco


Mobile relays

Mobile

Relay

Sink

(Base

station)

Mobile

Relay



Mario Di Francesco


Relocatable nodes

Sink

(Base

station)

Relocatable

node

Relocatable

node



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Mobile peers

Sink

(Base

station)



Mario Di Francesco


Overview of data collection

in WSNs with mobile elements

Data collection

– Exploits contacts

between nodes

– Different from classic WSNs

Three main phases

– Discovery

– Data transfer

– Routing to MEs

Mobile

elementStart of

contact

Communication

range of the

node reached

by the MS

End of

contact

Nodes

reachable

through

multi-hop

paths



Mario Di Francesco


Sparse wireless sensor networks

Reference scenario and sensor states

MDC in contact with at most one sensor at any time

Additional sleeping phase

Mobile data

collector

timeout

Data Transfer

Discovery

Sleeping

MDC out of reach or

communication over

timeout

MDC

discovered



Mario Di Francesco


Communication in sparse WNS

Nodes wait for the MDC to approach and then transfer data

Pros – Decreased message loss

– Nodes do not have to relay messages

– Tight synchronization is not required

Cons – Increased latency

– Cost of MDCs



Mario Di Francesco


Discovery phase

Asynchronous protocol with duty cycle

Mobile data collector

– Emits beacon messages periodically

Static sensor node

– Wakes up periodically to listen for incoming beacons

Node

MDC

...

...

TD

TON

Active

TOFF

TB

Evolution of sensing scenarios:

from sensors to phones and things

From sensors to smartphones

People-centric sensing applications

Internet of Things

Andrew T. Campbell et al., “The Rise of People-Centric Sensing”, IEEE Internet Computing 12(4):12–21, July

2008 (http://dx.doi.org/10.1109/MIC.2008.90)

L. Atzori, A. Iera, and G. Morabito, “The Internet of Things: A survey”, Computer Networks, 54(15):2787–

2805, October 2010 (http://dx.doi.org/10.1016/j.comnet.2010.05.010)

http://dx.doi.org/10.1109/MIC.2008.90



http://dx.doi.org/10.1016/j.comnet.2010.05.010

http://dx.doi.org/10.1016/j.comnet.2010.05.010



Mario Di Francesco



with mobile elements revisited

Mobile

Sink

Mobile

Sink



Mario Di Francesco


From sensor devices to smartphones

Smartphones as sensing platforms

– Abundance of sensors

Acceleration

Location, orientation

Sound, video

Proximity

– Rich in processing and storage resources

Enabling even computationally intensive applications

– Several wireless technologies

WiFi, Bluetooth (Low Energy)

Long range cellular radio

Near Field Communication (NFC)



Mario Di Francesco


People-centric sensing scenarios

Passive sensing scenarios

– People and communities are characterized

through data sampled by the phone during everyday life

Can be seen as a special case of WSNs with MEs or DTNs

– Also referred to as people-based sensing

Active sensing scenarios

– People involved in sensing campaigns

– Participants instructed to actively sense the environment

Sample applications: traffic/accidents monitoring, well being

Incentives for participation

– Also known as participatory sensing



Mario Di Francesco


The Internet of Things

Networked objects (devices)

– Deployed worldwide

– Connected over the Internet

IoT devices

– Individually addressable

– Interconnected and accessed through the standards of the web

– Not only sensors but also actuators (i.e., power switches)

Major issues

– Heterogeneity

– Scale

Computer Networks II – Advanced

Features (T-110.5111)

Mario Di Francesco, PhD

[email protected]


computer networks ii advanced features (t-110.5111) · wireless sensor networks with mobile...

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