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An Introduction to Wireless Sensor Networks

2014 Swedish Communication Technologies Workshop (Swe-CTW 2014),Malardalan University, June 2-5, 2014

Carlo FischioneAssociate Professor of Sensor Networks

e-mail:[email protected]://www.ee.kth.se/∼carlofi/

KTH Royal Institute of TechnologyStockholm, Sweden

June 5, 2014Carlo Fischione (KTH) An Introduction to Wireless Sensor Networks June 5, 2014 1 / 100

[email protected]

http://www.ee.kth.se/~carlofi/

Tutorial goal

After finishing the tutorial, you will know the essential networking andoptimization tools to cope with Wireless Sensor Networks (WSNs)

You will be able to design WSNs and learn recent selected researchdirections

Carlo Fischione (KTH) An Introduction to Wireless Sensor Networks June 5, 2014 2 / 100

Wireless Sensor Networks

Networking Wireless

Systems and Control

WirelessSensorNetworks


WSNs

Wireless sensor networks (WSNs) make Internet of Things possible

Computing, transmitting and receiving nodes, wirelessly networked together forcommunication, control, sensing and actuation purposes

Characteristics of WSNsI Battery-operated nodesI Short range wireless communicationI Mobility of nodesI No/limited central manager

Typical power consumption of a nodeCarlo Fischione (KTH) An Introduction to Wireless Sensor Networks June 5, 2014 4 / 100

Outline

Introduction

Medium Access Control

Routing

Cross-Layer Optimization

Distributed Optimization

Privacy Preserving Optimization


History of WSNs

DARPA DSN node, 1960

Mica2 mote, 2002

Tmote-sky, 2003

Smart Dust


Applications of WSNs

Environmental Monitoring

Transportation

Industrial Control

Healthcare


Smart Buildings

WSNs for controlling temperature, light, air and humidity, doors, alarms


Smart Buildings

By 2020, one of the most technological urban districts in the world

Thousands of Smart Buildings will be built


Structural Monitoring

Sensors used to measure response to traffic, tidal and seismic activity

Deployed on Golden Gate Bridge


Smart Energy Grids

source: http://deviceace.com/

Smart grids: Smart Grids: It’s All About Wireless Sensor Networks(http://stanford.wellsphere.com)


http://deviceace.com/

http://stanford.wellsphere.com

Water Pollution

The pollution level can be estimated by sensors on the water pipes

The estimates are reported centrally only when needed


WSNs in Industrial Automation

Added flexibility

I Sensor and actuator nodes can be placed more appropriatelyI Less restrictive maneuvers and control actionsI More powerful control through distributed computations

Reduced installation and maintenance costs

I Less cablingI More efficient monitoring and diagnosis


Distributed positioning

WSN allows to perform distributed camera calibration, positioning and tracking

Application: massive graphic effects in film production


Tutorial Overview

Application

Presentation

Session

Transport

Routing

MAC

Phy

Networking

Cross-Layer co-design

Optimization


Outline

Introduction


I Definition and classification of MACsI The IEEE 802.15.4 protocolI Mmwaves WSNs (the IEEE 802.15.3 protocol)

Routing





Next part contentApplication

Presentation

Session

Transport

Routing

MAC

Phy

When a node gets the right to transmit messages?

What is the Medium Access Control (MAC)?

What are the options to design MACs?

What is the MAC of IEEE 802.15.4?


Medium Access Control - MAC

MAC: mechanism for controlling when sending a message (packet) and whenlistening for a packet

MAC is one of the major component for energy expenditure in WSNs

I Receiving packets is about as expensive as transmittingI Idle listening for packets is also expensive

Typical power consumption of a node


Problems for MACs

1. Collisions: wasted effort when two packets collide

2. Overhearing: wasted effort in receiving a packet destined for another node

3. Idle listening: sitting idly and trying to receive when nobody is sending

4. Protocol overhead


The hidden terminal problem

Terminal, another word for node

Hidden terminal problem:

I Node A wants to send a packet to BI Node C wants to send a packet to DI Node A does not hear transmitter C sending packets that can be received by B

and D



A B C D




and D



A B C D

Transmit range:(depends on the channel, transmit power, )distance past which the SNR is in outage




and D



A B C D




I Node A wants to send a packet to B

I Node C wants to send a packet to DI Node A does not hear transmitter C sending packets that can be received by B

and D



A B C D




I Node A wants to send a packet to BI Node C wants to send a packet to D

I Node A does not hear transmitter C sending packets that can be received by Band D



A B C D





and D


The exposed terminal problem

A B C D

Carrier sense range:(depends on the channel, transmit power,...)distance within a transmitter can be heard/sensed at a receiver

Exposed terminal problem:

I B wants to send packet to AI C wants to send packets to DI Transmitter B hears transmitter C which is not causing collisions at the

receiver A. A is not in the transmit range of CI Transmitter C hears B, but D is not in the transmit range of B



A B C D

Transmit range of B Transmit range of C

Carrier sense range:(depends on the channel, transmit power,...)distance within a transmitter can be heard/sensed at a receiver






A B C D

Transmit range of B

Carrier sense range of B

Transmit range of C

Carrier sense range of CCarrier sense range:(depends on the channel, transmit power,...)distance within a transmitter can be heard/sensed at a receiver






A B C D

Transmit range of B


Transmit range of C



I B wants to send packet to A

I C wants to send packets to DI Transmitter B hears transmitter C which is not causing collisions at the




A B C D

Transmit range of B


Transmit range of C



I B wants to send packet to AI C wants to send packets to D

I Transmitter B hears transmitter C which is not causing collisions at thereceiver A. A is not in the transmit range of C

I Transmitter C hears B, but D is not in the transmit range of B



A B C D

Transmit range of B


Transmit range of C




receiver A. A is not in the transmit range of C

I Transmitter C hears B, but D is not in the transmit range of B



A B C D

Transmit range of B


Transmit range of C






MAC Classification for WSNs

Wireless Medium Access

Centralized

Schedule-based

Fixed assignment Demand

Contention-based

Distributed

Schedule-based

Fixed assignment Demand

Contention-based


Outline

Introduction


I Definition and classification of MACsI The IEEE 802.15.4 protocolI Mmwaves WSNs (the IEEE 802.15.3 protocol) and the association problem

Routing





IEEE 802.15.4 protocol architecture

Now we study the MAC of the standard IEEE 802.15.4

IEEE 802.15.4 is the de-facto reference standard for low data rate and low powerWSNs

Characteristics:

I Low data rate for ad hoc self-organizing network of inexpensive fixed, portableand moving devices

I High network flexibilityI Very low power consumptionI Low cost


IEEE 802.15.4 networks

IEEE 802.15.4 network composed of

I Full-function device (FFD)I Reduced-function device (RFD)

A network includes at least one FFD

The FFD can operate in three modes:

I A personal area network (PAN)coordinator

I A coordinatorI A device

An FFD can talk to RFDs or FFDs

RFD can only talk to an FFD


IEEE 802.15.4 network topologies

PAN CoordinatorPAN Coordinator

Star topologyPeer-to-peer topology

Communication Flow

Reduced Function Device

Full Function Device

3 types of topologies

I Star topologyI Peer-to-peer topologyI Cluster-tree


Cluster-tree topology

First PAN Coordinator

PAN Coordinator

Device


IEEE 802.15.4 physical layer

Frequency bands:

I 2.4 - 2.4835GHz GHz, global, 16 channels, 250KbpsI 902.0 - 928.0MHz, America, 10 channels, 40KbpsI 868 - 868.6MHz, Europe, 1 channel, 20Kbps

Features of the PHY layer

I Activation and deactivation of the radio transceiverI Transmitting and receiving packets across the wireless channelI Energy detection (ED, from RSS)I Link quality indication (LQI)I Clear channel assessment (CCA)I Dynamic channel selection by a scanning a list of channels in search of

beacon, ED, LQI, and channel switching


IEEE 802.15.4 physical layer

1.3

2[4]*1cm PHY(MHz) 2[4]*3cmFrequency band

(MHz) Spreading parameters Data parameters3-4 6-8 3cm Chip rate

(kchip/s) Modulation 3cmBit rate(kb/s) 3cm Symbol rate

(ksymbol/s) Symbols2[4]*868/915 868-868.6 300 BPSK 20 20 Binary

902-928 600 BPSK 40 40 Binary2[4]*1cm868/915

(optional) 868-868.6 400 ASK 250 12.5 20-bit PSSS902-928 1600 ASK 250 50 5-bit PSSS

2[4]*1cm868/915(optional) 868-868.6 400 O-QPSK 100 25 16ary Orthogonal

902-928 1000 O-QPSK 250 62.5 16ary Orthogonal2450 2400-2483.5 2000 O-QPSK 250 62.5 16ary Orthogonal

Frequency bands and propagation parameters for IEEE 802.15.4 physical layer


Physical layer data unit

SFD indicates the end of the SHR and the start of the packet data

PHR: PHY headerPHY payload < 128 byte


IEEE 802.15.4 MAC

The MAC provides two services:

I Data serviceI Management service

MAC features: beacon management, channel access, GTS management, framevalidation, acknowledged frame delivery, association and disassociation


Superframes

Superframe structure:

I Format defined by the PAN coordinatorI Bounded by network beaconsI Divided into 16 equally sized slots

Beacons

I Synchronize the attached nodes, identify the PAN and describe the structureof superframes

I Sent in the first slot of each superframeI Turned off if a coordinator does not use the superframe structure

Superframe portions: active and an inactive

I Inactive portion: a node does not interact with its PAN and may enter alow-power mode

I Active portion: contention access period (CAP) and contention free period(CFP)

I Any device wishing to communicate during the CAP competes with otherdevices using a slotted CSMA/CA mechanism

I The CFP contains guaranteed time slots (GTSs)


Bibliography

P. Park, P. Di Marco, P. Soldati, C. Fischione, K. H. Johansson, “A GeneralizedMarkov Model for an Effective Analysis of Slotted IEEE 802.15.4”, in Proc. of IEEE6th International Conference on Mobile Ad-hoc and Sensor Systems 2009 (IEEEMASS 09), Macau SAR, P.R.C., October 2009. Best Paper Award.

P. Park, S. Coleri Ergen, C. Fischione, A. Sangiovanni-Vincentelli, “Duty-CycleOptimization for IEEE 802.15.4 Wireless Sensor Networks”, ACM Transactions onSensor Networks, Vol. 10, No. 1, February 2014.

C. Fischione, P. Park, S. Coleri Ergen, “Analysis and Optimization of Duty-Cycle inPreamble Based Random Access Networks”, Springer Wireless Networks, Vol. 19,Issue 7, pp. 16911707, October 2013.

P. Park, C. Fischione, K. H. Johansson, “Modeling and Stability Analysis of HybridMultiple Access in IEEE 802.15.4 Protocol”, ACM Transactions on SensorNetworks, Vol. 9, No. 2, pp. 13:1–13:55, April 2013.

P. Di Marco, P. Park, C. Fischione, K. H. Johansson, “Analytical Modeling ofMulti-hop IEEE 802.15.4 Networks”, IEEE Transactions on Vehicular Technology,Vol. 61, No. 7, pp. 3191–3208, September 2012.


Outline

Introduction


I Definition and classification of MACsI The IEEE 802.15.4 protocolI Mmwaves WSNs (the IEEE 802.15.3 protocol)

Routing





Mmwaves communications

Figure: Millimeter-wave spectrum, Source: Zhouyue-Khan-2011

3-300GHz spectrum → mmW bands (λ ranges from 1-100mm)

60GHz band is an unlicensed spectrum

Large amount of spectral bandwidth: 7GHz

Achievable data rates > 2Gbps



Figure: Variation in Received Power with 32mW transmit power at 5.1GHz (left)and 60GHz (right), Source: Williamson-Athanasiadou-Nix-1997

Does not penetrate most solid materials → extra spatial isolation

Coverage is defined by the perimeter of the room

Frequency reuse is viable

Implicit security



Figure: Wafer-scale antenna: 64 elements in 8-12GHz (left) and 1024 elements in50-75GHz (right), Source: Mohamadi-2006

Antenna dimension ∝ λ

More antennas per fixed area

MIMO → higher beamforming gain / higher directivity

MIMO → SDMA → (point to multipoint communication)



Figure: Beam comparison

Narrow beams

Interference immunity

Deployment of multiple independent links in close proximity

Point-to-point mesh networks


Mmwaves 60 GHz Wireless Standards

IEEE 802.11ad

WiGig

IEEE 802.15.3c

WirelessHD

ECMA-387


Applications


Association Control and Relaying in60 GHz Wireless Access Networks



Goal: Maximizing the sum of clients’ throughput guaranteeingfair connection distribution for access points (AP)

Solution: Distributed algorithm for client-relay and client-access-point associationbased on auction algorithm

Results: Theoretical and numerical analysis


System Model

Client i ∈M = {1, . . . ,M}, relay j ∈ N = {1, . . . , N} and APk ∈ K = {1, . . . ,K}

Achievable rate at distance d is

R(d) =W log2

(1 +

PTGRGTλ2dη0

16π2(N0 + I)Wdη

)


W System bandwidth

λ Wavelength

d, d0 Distance, far field reference distance

η Path loss exponent (η ∈ [2, 6])

PT Transmission power of AP i to client j

GT ,GR Power gain of transmitter and receiver

N0 Power spectral density of the noise

I Broadband interferencei = 1 i = 3

i = 2

j = 1

23 (Q3)

45

6

7 8

9

10

R33R13

System Model

Throughput benefit from client i

a(i,k) = R(dik), a(i,j,k) = min{R(dij), R(djk)}

Total throughput

u =∑

(i,k)∈A

a(i,k)x(i,k) +∑

(i,j,k)∈A

a(i,j,k)x(i,j,k) ,

Binary decision variables x(i,k) = 1 if client i is associated to AP k and x(i,k) = 0otherwise

x(i,j,k) = 1 if client i is associated to relay j and then to AP k, or x(i,j,k) = 0otherwise


Resource Association Problem Formulation

maximizex(i,k), x(i,j,k)

u

s.t.∑

(i,k)∈A

x(i,k) +∑

(i,j,k)∈A

x(i,j,k) = 1, ∀i ∈M ,

∑(i,j,k)∈A

x(i,j,k) ≤ 1, ∀j ∈ N ,

x(i,j,k), x(i,k) = {0, 1}, ∀(i, j), (i, j, k) ∈ A ,

Variable: x(i,k), x(i,j,k)

Constraints: a) Client i needs to be assigned to one AP, b) Relay j can only beassigned to one client-AP pair, c) The decision variables are binary


i = 1 i = 3

i = 2

j = 1

23 (Q3)

45

6

7 8

9

10

R33R13

Bibliography

G. Athanasiou, C. Weeraddana, C. Fischione, L. Tassiulas, “Optimizing ClientAssociation in 60GHz Wireless Access Networks”, IEEE/ACM Transactions onNetworking, Accepted for Publication, 2014, to Appear.

G. Athanasiou, C. Weeraddana, C. Fischione, “Auction-based Resource Allocationin Millimeter-Wave Wireless Access Networks”, IEEE Communications Letters, Vol.17, No. 11, pp. 2108 2111, November 2013.


Outline

Introduction


Routing

I Classification of routing protocols for WSNsI The shortest path routingI Routing algorithms for standardized protocol stack





Previous partApplication

Presentation

Session

Transport

Routing

MAC

Phy

When a node gets the right to transmit?

What is the mechanism to get such a right?

How nodes are associated to access points?


Next SectionApplication

Presentation

Session

Transport

Routing

MAC

Phy

On which path messages should be routed?

What are the basic routing options?

How to compute the shortest path?

Which routing is used in standard protocols?


Routing protocols

Derive a mechanism that allows a packet sent from an arbitrary node to arrive atsome destination node

I Routing information: data structures (e.g., tables) on how a given destinationnode can be reached by a source node

I Forwarding: Consult these data structures to forward a given packet to itsnext hop node

Challenges

I Nodes may move, neighborhood relations change


Routing protocols classification

When the routing protocol operates?

1. Proactive: protocol always tries to keep its routing tables up-to-date and activebefore tables are actually needed

Example: Destination Sequence Distance Vector (DSDV), usesBellman-Ford algorithm (see below)

2. On demand: route is only determined when needed by a nodeExample: Ad hoc On Demand Distance Vector (AODV), nodes remember

where packets came from and populate routing tables accordingly

3. Hybrid: combine the previous two


But how paths are built and chosen?

We have seen a general classification of routing

In practice,

I How the routing structures (e.g., the tables) are built?I How the decision to select next hop is taken?


Many options for routing1. Path with minimum delay

2. Path with minimum packet error rate

3. Path with maximum total availablebattery capacity

I Path metric: Sum of batterylevels

I Example: A-C-F-H

4. Path with minimum battery cost

I Path metric: Sum of reciprocalbattery levels

I Example: A-D-H

5. Path with conditional max-min batterycapacity

I Only take battery level intoaccount when below a given level

6. Path with minimum variance in batterypower levels

7. Path with minimum total transmissionbattery powerCarlo Fischione (KTH) An Introduction to Wireless Sensor Networks June 5, 2014 54 / 100

Outline

Introduction


Routing






The shortest path routing

i

jts

source destination

The shortest path routing problem is a general optimization problem that modelsALL the cases above for routing

In the following, we study the basic version, when in the network there is one sourceand one destination

Multiple sources multiple destinations scenarios are a simple extension


Definitions

i

jts

source destination

N Number of nodesN Set of nodesA = {(i, j)} Set of arcsG = (N ,A) Networkaij Routing cost on the link ij

Examples: 1) MAC delay i→ j 2) packet error rate i→ j

What is the shortest (minimum cost) path from source s to destination t ?


The Shortest Path optimization Problem

minx

∑(i,j)∈A

aijxij

s.t.∑

j:(i,j)∈A

xij −∑

j:(j,i)∈A

xji = si

1 if i = s

−1 if i = t

0 otherwise

xij ≥ 0 ∀(i, j) ∈ A

x = [x12, x13, ..., xin , xin+1 , ...]

xij is a binary variable. It can be also real, but remarkably if the optimizationproblem is feasible, the unique optimal solution is binary

The optimal solution gives the shorstest path source-destination


The Shortest Path Optimization Problem

minx

∑(i,j)∈A

aijxij

s.t.∑

j:(i,j)∈A

xij −∑

j:(j,i)∈A

xji = si

1 if i = s

−1 if i = t

0 otherwise

xij ≥ 0 ∀(i, j) ∈ A

This problem is much more general and can be applied to

1. Routing over WSNs, used in ROLL RPL, WirelessHART...2. Project management3. The paragraphing problem4. Dynamic programming5. ...


How to solve the Shortest Path Problem

Since it is an optimization problem, one could use standard techniques ofoptimization theory, such as Lagrangian methods

However, the solution can be achieved by combinatorial algorithms that don’t useoptimization theory at all

We consider now such a combinatorial solution algorithm, the Generic shortest pathalgorithm

The Generic shortest path algorithm is the foundation of other more advancedalgorithms widely used for routing (e.g., in ROLL RPL) such as

1. Bellman-Ford method (see exercises)2. Dijkstra method (see exercises)


Complementary slackness conditionsfor the Shortest Path Problem

A label associated to a node

dj =

{a scalar

∞

PropositionLet d1, d2, ..., dN be scalars such that

dj ≤ di + aij , ∀(i, j) ∈ A

Let P be a path starting at a node i1 and ending at a node ik. If

dj = di + aij , ∀(i, j) of P

then P is a shortest path from i1 to ik.


Generic Shortest Path Algorithm: the idea in-nuce

Complementary Slackness conditions (CS) is the foundation of the generic shortestpath algorithm

Some initial vector of labels is assigned to nodes (d1, d2, ..., dN )

The arcs (i, j) that violate the CS condition dj > di + aij are selected and theirlabels redefined so that

dj := di + aij

This redefinition is continued until the CS condition dj ≤ di + aij is satisfied for allarcs (i, j)


Iterations of the Generic Shortest Path Algorithm

Let initially be V = {1} d1 = 0, di =∞, ∀i 6= 1

Iteration of the Generic Shortest Path Algorithm

Remove a node i from the candidate list V . For each outgoing arc (i, j) ∈ A, ifdj > di + aij , set

dj := di + aij

and add j to V if it does not already belong to V

The removal rule gives

Bellman-Ford method

Dijkstra method


An example

2

1 4

3

Origin

3

1

1 1

3

2

Iteration Candidate List V Node Labels Node out of V

1 {1} (0,∞,∞,∞) 12 {2, 3} (0,3,1,∞) 23 {3, 4} (0,3,1,5) 34 {4, 2} (0,2,1,4) 45 {2} (0,2,1,4) 2

∅ (0,2,1,4)

Generic shortest path algorithm [Bertsekas, 1998]


Convergence of the algorithm (a)

PropositionConsider the generic shortest path algorithm.

(a) At the end of each iteration, the following conditions hold:

If dj <∞, then dj is the length of some path that starts at 1 and ends at j

If i 3 V , then either di =∞ or elsedj ≤ di + aij , ∀j such that (i, j) ∈ A


Convergence of the algorithm (b)

Proposition

(b) If the algorithm terminates, then upon termination, for all j with dj <∞, dj is theshortest distance from 1 to j and

dj=

{min(i,j)∈A(di + aij) if j 6= 1

0 if j = 1


Convergence of the algorithm (c) (d)

Proposition

(c) If the algorithm does not terminate, then there exists some node j and a sequence ofpaths that start at 1, ends at j, and have a length diverging to −∞

(d) The algorithm terminates if and only if there is no path that starts at 1 and containsa cycle with negative length.


The convergence properties ofthe Generic Shortest Path Algorithm

The convergence properties above are based on sound theoretical analysis

They are the foundation over which routing protocols, such as the standardizedROLL RPL, are built

Let’s have a quick look at ROLL RPL and other standardized routing protocols


Outline

Introduction


Routing






ROLL: Routing over Low Power Lossy Networks

ROLL is a Working Group of the Internet Engineering Task Forcewww.ietf.org/dyn/wg/charter/roll-charter.html

ROLL RPL, IPv6 Routing Protocol for Low Power and Lossy Networks

RPL is intended for

I Industrial and home automationI HealthcareI Smart grids


www.ietf.org/dyn/wg/charter/roll-charter.html

ROLL RPL assumptions

Networks with many embedded nodes with limited power, memory, and processing

Networks interconnected by a variety of protocols, such as IEEE 802.15.4,Bluetooth, Low Power WiFi, wired or other low power Powerline communications

End-to-end Internet Protocol-based solution to avoid the problem ofnon-interoperable networks interconnected by protocol translation gateways andproxies

Traffic patterns

I Multipoint to Point (MP2P)I Point to Multipoint (P2MP)I Point-to-Point (P-2-P)


RPL is tree based

RPL constructs destination-oriented directed acyclic graphs (DODAGs) i.e., treessources-destinations

Nodes build and maintain DODAGs by periodically multicasting messages, theDODAG Information Object (DIO), to their neighbors

To join a DODAG, a node listens to the DIO messages sent by its neighbors andselects a subset of these nodes as its parents

Packet forwarding metrics, the aij see above:

1. Link reliability,2. Packet delay,3. Node energy consumption,4. Expected transmissions count (ETX)5. . . .


RPL DIO messages

DODAG minimizes the cost to go to the root (destination node) based on theObjective Function

DIO messages are broadcast to build the tree; DIO includes

I A nodes rank (its level) djI Packet forwarding metric aij

A node selects a parent based on the received DIO message and calculates its rank

Destination Advertisement Option (DAO) messages are sent periodically to notifyparent about routes to children nodes


Example

0

1 1

1

1 1 1

1

1 1 11

1


Example

0

1 1

1

1 1 1

1

1 1 11

1

DIO DIO

DIO


Example

0

1 1

1

1 1 1

1

1 1 11

11 1

1


Example

0

1 1

1

1 1 1

1

1 1 11

11 1

1DIO

DIO

DIO DIO


Example

0

1 1

1

1 1 1

1

1 1 11

11 1

1

2 2


Example

0

1 1

1

1 1 1

1

1 1 11

11 1

1

2 2

DIO DIO

DIO DIO


Example

0

1 1

1

1 1 1

1

1 1 11

11 1

1

2 2 3


Example

0

1 1

1

1 1 1

1

1 1 11

11 1

1

2 2 3

DIO

DIO

DIO DIO

DIO


Example

0

1 1

1

1 1 1

1

1 1 11

11 1

1

2 2

2

2


Example

0

1 1

1

1 1 1

1

1 1 11

11 1

1

2 2

2

2

3 3 33

2


Other standardized protocol stacks

ZigBee, www.zigbee.org

ISA SP-100, www.isa.org

WirelessHART, www.hartcomm.org


www.zigbee.org

www.isa.org

www.hartcomm.org

Bibliography

P. Di Marco, C. Fischione, K. H. Johansson, F. Santucci “Modeling Cross-LayerInteractions of IEEE 802.15.4 over Fading Channel”, IEEE Transactions on WirelessCommunications, to Appear, 2014.

P. Di Marco, G. Athanasiou, V. Mekikis, C. Fischione, “MAC and RoutingInteractions in Low Power and Lossy Networks”, Submitted to Computer Networks.

P. Di Marco, G. Athanasiou, C. Fischione, “Harmonizing MAC and Routing in LowPower and Lossy Networks”, in Proc. of IEEE Global TelecommunicationsConference 2013, (IEEE Globecom 13), Atlanta, GA, USA, December 2013.

P. Di Marco, G. Athanasiou, C. Fischione, “Harmonizing MAC and Routing in LowPower and Lossy Networks”, in Proc. of IEEE Global TelecommunicationsConference 2013, (IEEE Globecom 13), Atlanta, GA, USA, December 2013.

C. Fischione, S. C. Ergen, and C. Borean, “Method for Setting the OptimalOperation of a Routing Node of an Asynchronous Wireless CommunicationNetwork, Network Node and Communication Network Implementing the Method”,International Patent.


Outline

Introduction


Routing






Plant/Process

x(t)x(kh) sampled state

WSNIEEE 802.15.4

Wireless HART

Controlleru(t)

u(kh) sampled

actuationline

y(t)y(kh) sampled communication

line

How to co-design applications (e.g. control) and protocols?


Cross-Layer design

Energy consumption E (x)

minxE (x)

s.t. Pr (succ) ≥ 1− pPr (delay ≤ τmax) ≥ δ

x collects the protocol and control parameters


Cross-Layer design

Protocol parameters:radio powers, MACretransmissions,routing path, controldecisions...

Model methematicallythe protocol behaviour

Select the metrics(energy, delay, reliability)

Optimize (statically oron-line) the protocolparameters

Application requirements A highly efficient WSN

The role of mathematical modeling and optimization is central


Bibliography

U. Tiberi, C. Fischione, M. Di Benedetto, K. H. Johansson, “Energy-efficientSampling of Networked Control Systems over IEEE 802.15.4 Wireless Networks”,Automatica, Vol. 49, No. 3, pp. 712724, March 2013.

C. Fischione, P. Park, P. Di Marco, K. H. Johansson, “Design Principles of WirelessSensor Networks Protocols for Control Applications”, S. Mazumder Ed., Springer,Chapter 11, pp. 271299, April 2011.

P. Park, C. Fischione, A. Bonivento, K. H. Johansson, A. Sangiovanni-Vincentelli,“Breath: a Self-Adapting Protocol for Industrial Control Applications UsingWireless Sensor Networks”, IEEE Transactions on Mobile Computing, Vol. 6, No.6, pp. 821838, June 2011.

A. Bonivento, C. Fischione, L. Necchi, F. Pianegiani, A. Sangiovanni-Vincentelli,“System Level Design for Clustered Wireless Sensor Networks”, IEEE Transactionson Industrial Informatics, Vol. 3, No. 3, pp. 202214, August 2007. Best Paper ofthe IEEE Transactions on Industrial Informatics 2007.


Outline

Introduction


Routing





Distributed optimization

min f0(x)s.t. x ∈ X

At some “centralized” location, collect all primal variables of the network

Use primal information to calculate dual (Lagrangian) variables

Distribute dual variables over the network

Use dual variables when updating primal variables




xi

xj








λ








λ

λ






Fixed point solutions are powerfulInterference function optimization

s1

r1s2

r2

s3 r3

g11g21

Intended communicationInterference

Minimize the transmit powers whilemaintaining acceptable SINR

giipi∑i6=j

gijpj + ηi≥ γ

min ps.t. pi ≥ Ii(p) ∀i

If each interference function Ii is standard[Yates (95)], i.e.,

Monotonicity: If p ≥ p′, then I(p) ≥ I(p′)Scalability: For all α > 1, αI(p) > I(αp)

then pk+1 = I(pk) will converge to the optimal solution


Fast-Lipschitz optimization

Which problemsmax f0(x)s.t. xi ≤ fi(x) ∀i

can be solved by iteratingxk+1i = fi(x

k)?

Problem need not be convex

No need to compute, and communicate, dual variables

No central master, all nodes are peers

Nodes only need to evaluate their own constraint function


Definition of Fast-Lipschitz form

DefinitionA problem is on Fast-Lipschitz form if it can be written

max f0(x)s.t. xi ≤ fi(x) ∀i ∈ I

xi = fi(x) ∀i ∈ Ex ∈ D ⊆ <n

x = [x1, . . . , xn]T , f = [f1, . . . , fn]

T

f0 possibly vector valued, f0 : D ⊂ <n → <m, m ≥ 1

I and E are complementary subsets of {1, . . . , n}D is a box constraint,

D = {x ∈ <n |a ≤ x ≤ b}


Definition of Fast-Lipschitz problem


xi = fi(x) ∀i ∈ Ex ∈ D ⊆ <n

f(x) = [f1(x), . . . , fn(x)]T

DefinitionA problem is Fast-Lipschitz when it can be written on Fast-Lipschitz form and, iffeasible, admits a unique Pareto optimal solution x?, uniquely determined by the systemof equations

x? = f(x?).

Fast-Lipschitz optimization is an alternative to dual-based methods that

I is easily distributed, low coordinationI has a low cost for communicationI is computationally cheap


Main result


xi = fi(x) ∀i ∈ Ex ∈ D ⊆ <n

TheoremWhen a problem on Fast-Lipschitz form is feasible, with f0 and f fulfilling the Qualifyingconditions, the problem is Fast-Lipschitz, i.e., the unique Pareto optimal solution is givenby

x? = f(x?).

The optimal solution can then be found in a distributed manner, by iterating theconstraint functions:

xk+1 =[f(xk)

]D, or xk+1

i =[fi(x

k)]D

Qualifying conditions are sets of assumptions on f0, f and D which ensure that theproblem is Fast-Lipschitz (sufficient but not necessary)


Applications of Fast-Lipschitz problem

Fast-Lipschitz optimization is an alternative to dual-based methods for resourceallocation over wireless sensor networks and wireless networks in general

Radio power control: Interference function, Type I, Type II, functions

Distributed detection

Application to distributed estimation


Bibliography

C. Fischione, “Fast-Lipschitz Optimization with Wireless Sensor NetworksApplications”, IEEE Transactions on Automatic Control, Vol. 56, No. 10, pp. 23192331, October 2011.

M. Jakobsson, C. Fischione, C. Weeraddana, “Extensions of Fast-LipschitzOptimization”, Submitted to IEEE Transactions on Automatic Control,http://arxiv.org/abs/1309.0462.

M. Jakobsson, C. Fischione, “Optimality of Radio Power Control Algorithms viaFast-Lipschitz Optimization”, Submitted to IEEE Transactions on InformationTheory, http://arxiv-web3.library.cornell.edu/abs/1404.4947.

A. Speranzon, C. Fischione, K. H. Johansson, A. Sangiovanni-Vincentelli, “ADistributed Minimum Variance Estimator for Sensor Networks”, IEEE Journal onSelected Areas in Communications, special issue on Control and Communications,Vol. 26, No. 4, pp. 609621, May 2008.

P. C. Weeraddana, M. Codreanu, M. Latva-aho, A. Ephremides and C. Fischione,“A Review of Weighted Sum-Rate Maximization in Wireless Networks”, NOWFoundations and Trends in Networking, Vol. 6, No 1-2, pp. 1–163, 2012.


http://arxiv.org/abs/1309.0462

http://arxiv-web3.library.cornell.edu/abs/1404.4947

Outline

Introduction


Routing





Motivation – Why Privacy/Security ?

social networks

healthcare data

e-commerce

banks, and government services


Real World

example 1

- hospitals coordinate ⇒ inference for better diagnosis- larger data sets ⇒ higher the accuracy of the inference- challenge: neither of the data set should be revealed


anchor

data set 1 data set 2

data set 3hospital 1 hospital 2

hospital 3

Real World

example 2

- cloud customers outsource their problems to the cloud- challenge: problem data shouldn’t be revealed to the cloud- secured voting systems


anchor

CLOUD

cloud customer 1

cloud customer 2

cloud customer 3

cloud customer 4


solve, in a secured manner, the n-party problem of the form:

f( ~A1, . . . , ~An) = inf~x∈{~x|~g(~x, ~A1,..., ~An)�~0}

f0(~x1, . . . , ~xn, ~A1, . . . , ~An)

- ~Ai is the private data belonging to party i- ~x = (~x1, . . . , ~xn) is the decision variable- f0(·) is the global objective function- ~g(·) is the vector-valued constraint function- f(·) is the desired optimal value

can we perform such computations with “acceptable“ privacy guaranties ?


Overview

Secured Multiparty

Computation

Cryptographic

Methods

Non-Cryptographic Methods:

Optimization Methods

Quantify Privacy ?

Disguise Data

Unified Framework ?

Novel Approaches

Mathematical Decomposition ?

ADMM ?


Overview

Secured Multiparty

Computation

Secured Multiparty

Computation

Cryptographic

Methods



Quantify Privacy ?

Disguise Data

Unified Framework ?

Novel Approaches


ADMM ?


Overview

Secured Multiparty

Computation

Secured Multiparty

Computation

Cryptographic

Methods

Cryptographic

Methods



Quantify Privacy ?

Disguise Data

Unified Framework ?

Novel Approaches


ADMM ?


Overview

Secured Multiparty

Computation

Secured Multiparty

Computation

Cryptographic

Methods

Cryptographic

Methods



Quantify Privacy ?



Quantify Privacy ?

Disguise Data

Unified Framework ?

Novel Approaches


ADMM ?


Overview

Secured Multiparty

Computation

Secured Multiparty

Computation

Cryptographic

Methods

Cryptographic

Methods



Quantify Privacy ?



Quantify Privacy ?

Disguise Data

Unified Framework ?

Disguise Data

Unified Framework ?

Novel Approaches


ADMM ?


Overview

Secured Multiparty

Computation

Secured Multiparty

Computation

Cryptographic

Methods

Cryptographic

Methods



Quantify Privacy ?



Quantify Privacy ?

Disguise Data

Unified Framework ?

Disguise Data

Unified Framework ?

Novel Approaches


ADMM ?

Novel Approaches


ADMM ?


General Formulation

we pose the design or decision making problem

minimize f0(~x)subject to fi(~x) ≤ 0, i = 1, . . . , q

~C~x− ~d = ~0

(1)

optimization variable is ~x ∈ Rn

fi, i = 0, . . . , q are convex

~C = [~ci] ∈ Rp×n with rank(~C) = p

~d = [di] ∈ Rp

How to solve the problem in a privacy preserving manner?

1. Transformation of decision variables2. Transformation of objective and constraint functions


12

3

4

5

q

f1, ~c1, d1f2, ~c2, d2

f3, ~c3, d3

f4, ~c4, d4

f5, ~c5, d5

fq,~cq, dq

Bibliography

C. Weeraddana, G. Athanasiou, C. Fischione, J. Baras, “Per-se Privacy PreservingSolution Methods Based on Optimization”, in Proc. of IEEE 52nd Conference onDecision and Control 2012 (IEEE CDC 13), Florence, Italy, December 2013.

C. Weeraddana, G. Athanasiou, C. Fischione, J. Baras, “Per-se Privacy PreservingDistributed Optimization”, Submitted to IEEE Transactions on Automatic Control,http://arxiv.org/abs/1210.3283


http://arxiv.org/abs/1210.3283

Conclusions

Introduction


Routing





Acknowledgements

Chathuranga Weeraddana, George Athanasiou, Martin Jakobsson, Pangun Park,Piergiuseppe Di Marco, Yuzhe Xu


An Introduction to Wireless Sensor Networks

2014 Swedish Communication Technologies Workshop (Swe-CTW 2014),Malardalan University, June 2-5, 2014

Carlo FischioneAssociate Professor of Sensor Networks

e-mail:[email protected]://www.ee.kth.se/∼carlofi/

KTH Royal Institute of TechnologyStockholm, Sweden

June 5, 2014Carlo Fischione (KTH) An Introduction to Wireless Sensor Networks June 5, 2014 100 / 100

[email protected]

http://www.ee.kth.se/~carlofi/

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