Energy Conservation in
Wireless Sensor Networks
with Mobile Elements
Giuseppe Anastasi
Pervasive Computing & Networking Lab (PerLab)
Dept. of Information Engineering, University of Pisa
E-mail: giuseppe.anastasi@iet.unipi.it
Website: www.iet.unipi.it/~anastasi/
PerLabCOST Action IC0804 Training School – Palma de Mallorca, Spain, April 24-27, 2012

Overview
PerLabWSN-MEs
Power Management & Node Discovery
Schedule-based
On demand
Asynchronous
Fixed
Adaptive (Learning-based, Hierarchical)

Conclusions and Research Questions

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Wireless Sensor Networks
with Mobile Elements

Static Sensor Networks
PerLabFunneling Effect!
42

Other advantages of using WSN-MEs
PerLabConnectivity
A sparse sensor network may be a feasible solution for a large
number of applications

Cost
Reduced number of sensor nodes

reduced costs

Reliability
Single-hop communication instead of multi-hop
communication
Reduced contentions/collisions and message losses

Energy Conservation in Static and Mobile WSNs

55

Components of a WSN-ME
PerLabRegular Sensor Nodes
Sensing (source of information)
Data Forwarding
May be Static or MobileSink Nodes (Base Stations)
Destination of Information
Collect information directly or through intermediate nodes
May be Static or MobileSpecial Support Nodes
Neither source nor destination of information
Perform a specific task (e.g., data relaying)
Typically mobile
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Mobile Elements
PerLab

Relocatable Nodes
Limited mobility
Do not carry data while moving
Typically used in dense networks

Mobile Data Collectors
Mobile Sinks
Mobile Relays

Mobile Peers
Regular mobile nodes

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Relocatable Nodes
PerLab84

Mobile Sinks
PerLab9Mobile Relays
PerLab105

Mobile Sink/Relay: Potential Applications
PerLab

Air Quality Monitoring in Urban Areas
Sensors in strategic locations along streets.
Mobile Nodes are on board of buses
Collect data and transport to the sink nodeBus

Urban Sensing Applications
Mobile nodes are personal devices

Sensor-to-vehicle communication…

11Mobile Peers
PerLab126

Mobile Peers
PerLabN. Lane, E. Miluzzo, H. Lu, D. Peebles, T. Choudhury, A. Campbell, A Survey of Mobile Phone Sensing, IEEE
13Communication Magazine, Sept. 2010.

Mobile Peers: Potentialapplications

PerLabMobile devices equipped with(mobile) sensors
Camera, audio recorder, accelerometer, …

Wireless communication
3G, WiFi, Bluetooth, …

Can be used to implement
Personal Sensing applications (e.g., Cence me)Group Sensing applications (e.g., garbage watch)Participatory sensing applications

N. Lane, E. Miluzzo, H. Lu, D. Peebles, T. Choudhury, A. Campbell, A Survey of Mobile Phone Sensing, IEEE
14Communication Magazine, Sept. 2010.

7

Energy conservation in WSN-MEs
PerLabData-driven approaches
data compression
data prediction…

Power Management (duty cycling)
The sensor duty cycle should be as low as possible
to maximize the lifetime

Contacts could be missed
Efficient ME Discovery
Maximize the number of detected contacts while minimize energy
consumption

Energy Conservation in Static and Mobile WSNs1515Power Management andMobile Element Discovery
How to detect all potential contacts
while minimizing the energy
consumption at sensors?

8Ideal Scenario
PerLabMESensor
Node17

In practice
PerLabMDC arrival times are typically not known inadvanceSensors nodes cannot be always active
Low duty cycle δ to save energy

Discovery Protocol
Strictly related with power management

189

Power Management Schemes
PerLab19

Scheduled Rendez-vous schemes
PerLabSensor nodes and ME agree on the visit timeat least with some approximation

Simple to implement and energy Efficient
Synchronization required
Not applicable in some contexts
timeoutSleeping

Communication

ME departure orcommunication over
Chakrabarti, A., Sabharwal, A., and Aazhang, Using Predictable Observer Mobility for Power Efficient
Design of Sensor Networks”, Proc. International Workshop on Information Processing in Sensor
Networks (IPSN 2003), Pages 129-145.
2010On-demand schemes
PerLabThe ME wakes up the static node when it is nearby

Passive wakeup radio
Use energy harvested by the wakeup radio (e.g., RFID)

Active wakeup radio
Low-power radio + high-power radio

21

Passive Wakeup radio
PerLabUse the energy passively received through thewakeup radio to activate the data radio
Very limited distance
Few meters (suitable only for robotic networks)
The distance can be increased at the cost of– Increased complexity on the wakeup radio (increasedcost)– Increased wakeup time

Additional hardware required
H. Ba, I. Demirkol, W. Heinzelman, Feasibility and Benefits of Passive RFID Wakeup Radios for
Wireless Sensor Networks, Proc. IEEE Globecom 2010, Miami, Florida, USA, Dec. 6-10, 2010
L. Gu, J. Stankovic, Radio-Triggered Wake-up for Wireless Sensor Networks, Real-Time SystemsJournal, Vol. 29, pp. 157-182, 2005.
2211

Passive Wakeup RadioPerLabWISP
Wireless Identification and Sensing
Platform
Integration of Tmote Sky mote witha passive RFID tag
RFID reader on the ME
Maximum distance: few meters

23

Active Wakeup RadioPerLabRadio Hierarchy
Scenario
Mobile opportunistic network of handheld devices

Multiple-radio strategy
Higher-level radio for data exchange, lower-level radio fordiscovery
Bluetooth and WiFi, Mote and WiFi

The lower-level radio is used to discover, configure andactivate the higher-level radio
Bluetooth used to discover a nearby WiFi Access Point or node andconfigure the WiFi interface

T. Pering, V. Raghunathan, R. Want, Exploiting Radio Hierarchies for Power-Efficient Wireless Device
Discovery and Connection Setup, Proc. International Conference on VLSI Design, 2005
2412

Active Wakeup RadioPerLabHierarchical Power Management
Scenario
Opportunistic networks of handheld devices
WSNs with all mobile nodes

Multiple radio’s strategy
Low- power radio for discovery
High-power radio for both discovery and data exchange
High-power radio is awakened by the low-power radio
E.g., mote radio and WiFi

[Jun09] H. Jun, M. Ammar, M. Corner, E. Zegura, Hierarchical Power Management in Disruption Tolerant
Networks with Traffic-aware Optimization, Computer Communications, Vol. 32 (2009), pp. 1710-1723
25

Active Wakeup RadioPerLabNetwork Interrupts
ScenarioSensor Networks (with MEs)

Two different radios
A primary high-power radio usually in sleep mode
Used for data communication

Control Low-power radio always powered on
Used for control messages

A node can activate the high-power radio of a nearby
node by sending it a beacon through the low-powerradio
J. Brown, J. Finney, C. Efstratiou, B. Green,N. Davies, M. Lowton, G. Kortuem, Network Interrupts:
Supporting Delay Sensitive Applications in Low Power Wireless Control Networks, Proc. ACM
Workshop on Challenged Networks (CHANTS 2007), Montreal, Canada, 200726

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Limits of On-demand schemes
PerLabOn-demand schemes require multiple radios
which may not available in current sensor
platforms

The range of the wakeup radio is typically limited
Few meters for passive radios

Active radios have a longer range, but they
consume energy
The energy consumption should be below 50 µW
And the wakeup range should be as long as thecommunication range

27Power Management Schemes
PerLab28

14

Asynchronous schemes
PerLabME emits periodic beacons to announce itspresence
SN wakes up periodically (period listening), andfor short periods
Very low duty cycle for saving energy

29

Asynchronous (Periodic Listening)
PerLab

TON= TB + TD
δ = TON/(TON + TOFF)
3015

Classification of Periodic Listening Schemes
PerLab31

Classification of PeriodicListening Schemes
PerLabFixed Schemes
Both the beacon period and the sensor node’s duty cycle
are fixed over time

Adaptive Schemes
Learning-based schemes
The arrival time of the ME is predicted based on the past history,
and the duty cycle is adjusted accordingly

Hierarchical schemes
Two different operation modes for sensor nodes– Low-power mode (most of the time)– High-power mode (when the ME is nearby)

32

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Fixed Schemes
PerLabFixed Beacon Period
Fixed Sensor’s Duty Cycle (δ)
A low duty cycle saves energy, but contacts may be missed
A high duty cycle increases the % of detected contacts, butdecreases the sensor’s lifetime

Key Question
Which is the optimal duty cycle that allows to detect allcontacts with the minimum energy expenditure?
The optimal duty cycle depends on a number of factorsthat are difficult (if not impossible) to know in advance.

G. Anastasi, M. Conti, M. Di Francesco, Reliable and Energy-efficient Data Collection in Sparse Sensor
Networks with Mobile Elements, Performance Evaluation, Vol. 66, N. 12, December 2009.
33

Fixed Schemes
PerLabFixed approach
Fixed Beacon Period
Fixed Sensor’s Duty Cycle (δ) [Mat05] [Jai06]
A low duty cycle saves energy, but contacts may be missed
A high duty cycle increases the % of detected contacts, but
decreases the sensor’s lifetime

This approach is quite inefficient, especially whensensor nodes spend a long time in the discovery phase[Mat05] R. Mathew, M. Younis, S. Elsharkawy Energy-Efficient Bootstrapping Protocol for WirelessSensor Network, Innovations in Systems and Software Engineering, Vol. 1, No 2, Sept. 2005
[Jai06] S. Jain, R. Shah, W. Brunette, G. Borriello, and S. Roy, Exploiting Mobility for Energy Efficient Data
Collection in Wireless Sensor Networks, Mobile Networks and Applications, Vol. 11, No. 3, June 2006.
3417Learning-based approaches
PerLabAdaptive Beacon Rate
Reference Scenario
All sensor nodes are mobile
Fixed sink with limited energy budget
Energy harvesting

Basic idea
Adaptive beacon emission rate
Time is divided in slots (1-hour duration)
For each time slot the expected contact probability is derived andupdated dynamically based on the past history
The beacon emission rate is varied according to the estimatedprobability and the available energy

Based on reinforcement learning
V. Dyo, C. Mascolo, Efficient Node Discovery in Mobile Wireless Sensor Networks, Proc. DCOSS 2008,
LNCS, vol. 5067. Springer, Heidelberg (2008)
35

Learning-based approaches
PerLabResource-Aware Data Accumulation (RADA)
Reference Scenario
Static Sensor Nodes (with energy limitations)MEs are resource-rich devices

Basic idea
Fixed (Periodic) Beacon Emission by ME
The wake-up period (i.e., duty cycle) of the sensor node is adjusted
dynamically, depending on the past history
Based on DIRL framework

DIRL framework
Based on Q-learning
Autonomous and adaptive resource management
suitable to sparse WSNs
K. Shah, M. Di Francesco, G. Anastasi, M. Kumar, A Framework for Resource-Aware Data Accumulation inSparse Wireless Sensor Networks, Computer Communications, Vol. 34, N. 17, November 2011.
36

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DIRL framework
PerLabSet of tasks to be executed
Task priority
Applicability predicate

Set of states
State representation includes system and application variables
Hamming distance used for deriving distance between states andaggregate similar states

Utility Lookup Table: Q(s, t)
Q(s,t) gives the long-term utility of executing task t in state s

Exploration/Exploitation strategy
Exploration with probability εA random task is executedExploitation with probability 1−εThe best task, according to Q-values, is selected
K. Shah, M. Kumar, Distributed Independent Reinforcement Learning (DIRL) Approach to Resource
Management in Wireless Sensor Networks, Proc. IEEE International Conference on Mobile Adhoc and37
Sensor Systems (MASS07), Pisa, Italy, October 2007

DIRL Algorithm
PerLabQ(s,t)= (1−α)Q(s,t)+α(r+γe(s’))

K. Shah, M. Kumar, Distributed Independent Reinforcement Learning (DIRL) Approach to Resource
Management in Wireless Sensor Networks, Proc. IEEE International Conference on Mobile Adhoc and38
Sensor Systems (MASS07), Pisa, Italy, October 2007

19

Simulation Results
PerLabSparse Scenario

K. Shah, M. Di Francesco, G. Anastasi, M. Kumar, A Framework for Resource-Aware Data Accumulation inSparse Wireless Sensor Networks, Computer Communications, Vol. 34, N. 17, November 2011.
39

Limits of Adaptive Schemes
PerLab

Learning-based schemes perform well when
the ME has a regular mobility pattern
The regularity can be learned and exploited forpredicting next arrivals

Performance degrades significantly as therandomness in the mobility pattern increases

4020

Hierarchical Discovery schemes
PerLabBasic idea
The duty cycle is adjusted dynamically (as in learning-basedapproaches)
Low duty cycle when the ME is far
High duty cycle when the ME is about to arrive

Information about the ME location are provided by the ME itself

Dual Radio
Low-power radio for discovery and a high-power radio for datacommunication
Already considered as on-demand schemes

Dual Beacon
Long-range beacons for announcing the presence of the ME in
the area
Short-range beacons for informing that communication cantake place
41

Dual Beacon Discovery (2BD)
PerLabME uses two different beacon messages
Long-range beacons (LRB) for announcing the presence of the ME inthe area
Short-range beacons for informing that communication can takeplaceSensor nodes alternate between two dutycycles
Typically in Low duty cycle
Switch to High duty cycle upon receiving a LRBEnter the communication phase upon receiving a SRB
Switch back to Low duty cycle at the end of the communication
phase
F. Restuccia, G. Anastasi, M. Conti, and S. Das, Performance Analysis of a Hierarchical Discovery
Protocol for WSNs with Mobile Elements, Proc. IEEE International Symposium on a World of Wireless,Mobile, and Multimedia Networks (WoWMoM 2012), San Francisco, CA, USA, June 25-28, 2012.
K. Kondepu, G. Anastasi, M. Conti, Dual-Beacon Mobile-Node Discovery in Sparse Wireless Sensor
Networks, Proc. IEEE International Symposium on Computers and Communications (ISCC 2011), Corfu,
42
Greece, June 28 – July 1, 2011.

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2BD Protocol
PerLab43

Simulation Results
PerLabSparse Scenario

150-200 µW

4422

False Activations
PerLabR 
E FA =  − 1 ⋅ Tout ⋅ [δ H ⋅ PRX + (1 − δ H ) ⋅ PSL ]r45

Simulation Results
PerLabSparse Scenario

Dense Scenario(false activations never occur)(false activations may occur)

46

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Conclusions&Key Research Questions

SummaryPerLab48

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Summary
PerLabSchedule-based power management can be used
only in some special cases
On-demand wakeup is pretty interesting!
However …
Active wakeup radio consume energy
Low power consumption * long time = large energy consumption

Passive wakeup radios do not consume additional energy,
but they have very very short ranges (few meters)
In both cases, special hardware is required

49

Summary
PerLab

Periodic Listening can be always usedAs it does not require special hardware
Finding the appropriate parameters may not be so easy
Using fixed parameters may result in inefficient solutions

Periodic Listening with adaptive parameters ismore efficient
Learning-based schemes are suitable for scenarios where
ME moves with a regular pattern
Hierarchical schemes (based on dual beaconing) are more
flexible
False activations may occur in dense scenarios

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Key Research Question
PerLabIs there any room for new research activities?
Adaptive strategies
More complex (and efficient) adaptive strategies can beinvestigated
Adaptive strategies forEnergy conservation + energy harvesting = unbounded lifetime

Optimization over multiple parameters
Data generation process
ME arrival pattern (next arrival)
Available space in the local buffer
Available energy (energy harvesting)51

Key Research Question
PerLabIs there any room for new research activities?
WSN with all mobile nodes (opportunistic networks)
In opportunistic networks a lot of work has been
done for data dissemination
Less attention has been devoted to node discovery(related with power management)
Although nodes spend most of time for discovery (ratherthan for data dissemination).

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ReferencePerLabM. Di Francesco, S. Das, G. Anastasi, Data Collection
in Wireless Sensor Networks with Mobile Elements: ASurvey,Survey ACM Transactions on Sensor Networks, Vol.
8, N.1, August 2011.
Available at
http://info.iet.unipi.it/~anastasi/

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