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Advanced Topics on Wireless Ad Hoc Networks

Lecture 1: Introduction / Data propagation inWireless Sensor Networks I

Sotiris NikoletseasProfessor

CEID - ETY Course2017 - 2018

Sotiris Nikoletseas, Professor Introduction / Data propagation in Wireless Sensor Networks I 1 / 61

Summary of this Lecture

A. Types of wireless ad hoc networks

B. Introduction to Wireless Sensor Networks (characteristics,applications, critical challenges, algorithmic properties)

C. Thematic structure of the 2017-2018 Course

D. The problem of data propagation in WSNE. Two protocols for data propagation:

1. Directed Diffusion (data-centric)2. LEACH (cluster-based)


Wireless Networks - Taxonomy

Fixed, single-hop networks: fixed infrastructure, centralizedcontrol

cellular networkssatellite networkswireless LANs

Ad hoc, multi-hop networks:no fixed infrastructureno centralized controlself-organization

In this course, we will study only Wireless Ad Hoc Networks.


A. Types of Wireless Ad Hoc Networks

1. Wireless Sensor Networks (WSN)

2. Wireless Rechargeable Sensor Networks (WRSN)

3. Mobile Ad Hoc Networks (MANETs)

4. Distributed Autonomous Mobile Robots

5. Mobile Robotic Sensor Networks

6. Radio Networks

7. Bluetooth Networks


1. Wireless Sensor Networks (WSN)

small size nodes, limited computing power, limited memory

low cost / large number of such nodes

equipped with diverse sensors (temperature, light, motion, etc.)

operate on small battery

static or mobile nodes

wireless communication

Sensor nodes Sensor field

Control Center


2. Wireless Rechargeable Sensor Networks (WRSN)

sensor nodesuse rechargeable batteriesare equipped with wireless power receiver

Chargersmay be either mobile or statichave significant energy reservesare equipped with wireless power transmitter

⇒ are able to charge the sensor nodes wirelessly


3. Mobile Ad Hoc Networks (MANETs)

nodes “stronger” than in WSN, faster CPU, more memory (e.g.laptops)

high mobility ⇒ dynamic topology

wireless communication (lower capacity than wired links)

lack of central control

multi-hop routing

limited energy (battery or similar)


4. Distributed Autonomous Mobile Robots

many, very simple, generic, identical, autonomous micro-robots

robots are autonomous (no central control), homogenous (samehardware, run same software), anonymous (indistinguishable)

no communication capabilities

each robot alone is computationally weak, cooperation of robotsis essential to perform complex tasks

algorithmic issue: minimum level of individual capabilities(memory, visibility, common coordinate system etc) needed forthe system as a whole to solve a given task, i.e. how much localpower is needed perform global tasks?

fundamental tasks: gathering, pattern formation


5. Mobile Robotic Sensor Networks

nodes with motorial capabilities: tiny, small robots than canmove

equipped with sensors

lack of wireless communication ⇒ only based on what nodes“see”(vision), via their sensors.⇓3 key features of robotic sensor nodes:

mobile, like MANETs, mobile WSNsilent (no communication), like traditional robotsmyopic (limited visibility via their sensing range), like WSN

energy is a concern but not as crucial as in mobile WSN.

typical applications: search and rescue missions, spaceexploration, military operations, dangerous area surrounding.


6. Radio Networks

collection of transmitter-receiver devices (called nodes)

each node can reach a given subset of other nodes depending onthe power of its transmitter and on topographic characteristics ofthe network region.

typical radio nodes: portable (hand-held like walkie-talkie),mobile (taxi radio unit), fixed radio (in some headquarters, officeetc.).

scale: big area, city, state/province, country

typical applications: rescue operations after a disaster, military,public services (police, fire etc.)

fundamental communication task: broadcasting i.e. transmit amessage from one node (source) to all other nodes.


7. Bluetooth Networks

short range (a few meters) personal area network (PAN)

master/slave operation: One master may communicate with up toseven slaves in a piconet (ad-hoc bluetooth network)

two or more piconets can be connected to form a scatternet

low power consumption (short range, low cost tranceivermicrochips in each device), easy discovery and setup

typical devices (in telephones, tablets, headsets, console gamingequipment, smart watches etc.)


B. What is a Wireless Sensor Network?

very large number of tiny “smart” sensors

severe limitations


densely / randomly deployed in an area

self-organization

co-operation

locality

⇒ An “ad-hoc” wireless network for:

sensing crucial events and ambient conditions

data propagation


A Sensor Network - Graphical Representation

Sensor nodes Sensor field

Control Center


“Smart” Sensor Nodes

very small size

operate on a small battery

multifunctional sensing


limited computing power / limited memory

low cost


Technical Specifications of Current Sensors

TPR2420 Specifications (TelosB motes)

Dimensions 6.5cm × 3.1cm × 2cmCPU 8 MHz Texas Instruments MSP430Memory 10KB RAM, 48KB Program flash, 16KB EEPROM, 1MB flashPower Supply 2X AA batteriesProcessor Current Draw 1.8 mA (active current)

< 5.1 µA (sleep mode)Interface USBNetwork Wireless 250 Kbps at 2.4GHz (802.15.4)

Radio range depends on configuration and locationIntegrated Sensors Visible Light and IR, Humidity, Temperature


Technical Specifications of Current Sensors

TinyOS Key Facts

Current Version TinyOS 2.0.2Users Over 500 research groups and companiesSoftware Footprint 3.4 KbArchitecture Component-basedHardware Supported 8 different mote typesPeak load CPU Usage < 50 %Prog. Language Used NesCComponents provided Sensor Interface

Basic Multihop RoutingRadio Communication


Examples of Sensor Motes

Mica2TelosB / Tmote Sky


Smart vs Traditional Sensors

In traditional sensors:

sensors perform only sensing

sensor positions are carefully engineered

deployed far from the area of interest

data processing only at some central nodes

larger in size

constant power supply


The First Projects (I)

“Smart Dust” (Berkeley) - 1998primary goal: a few mm3 sizelaser communicationmotes developmentTinyOS

(http://robotics.eecs.berkeley.edu/∼pister/SmartDust/)


The First Projects (II)

WINS (“Wireless Intergrated Network Sensors”) (UCLA) - 1993focus : RF communicationdevelopment of low power MEMS

(http://www.seas.ucla.edu/∼pottie/papers/smallWINS_ACM.pdf )

“Ultralow Power Wireless Sensor” (MIT)primary goal : extremely low power consumptionpower levels : 10 µW to 10 mWRF communication


Current Major Projects (I) - Partial List

WEBS (“Wireless Embedded Systems”, David Culler)(Berkeley)vision: “. . . highly embedded and networked devices, ofteninteract with people and their physical environment, and arehighly constrained in resources such as computation and energy. . .”.several subprojects (SMAP, blip, ACme, Motescope Testbed,tinyOS, LoCal)

(http://smote.cs.berkeley.edu:8000/tracenv/wiki)

NEST (“Network Embedded Systems Technology”)originally a DARPA funded project on middleware services andnovel applications of WSNs. Today, an umbrella projectencompassing diverse ongoing research activities in the WSNdomain.vision: “Ambient sensing and computing in the real world”.includes several major groups (Berkeley, Virginia, Vanderbilt etc.)


Current Major Projects (II) - Partial List

CENS(“Center for Embedded Networked Sensing”, Deborah Estrin)(UCLA)

an NSF Science and Technology Center (2001-2011)research topics: multiscaled automated sensing, terrestrialecology observing systems (TEOS), aquatic observing systems,embeddable sensors

(http://www.cens.ucla.edu)

NESL (“Networked & Embedded Systems Laboratory”, ManiSrivastava)(UCLA)

wireless sensor and actuator networks, low power networking,mobile and wireless computing, pervasive computing

(http://nesl.ee.ucla.edu)

NMS (“Networks and Mobile Systems”, Hari Balakrishnan)(MIT)topics: wireless networks, P2P networks, sensor networks,networked systems

(http://nms.csail.mit.edu)Sotiris Nikoletseas, Professor Introduction / Data propagation in Wireless Sensor Networks I 22 / 61

The Low Energy Challenge

In a cubic millimeter volume⇒ current best battery : 1 Joule

For continuous operation over 1 day :

⇓power consumption ' 10 µW

⇒{

efficient power management strategies neededefficient networking algorithms required


RF vs Laser Communications

Radio Frequency :small mote size ⇒ limited space for antennas ⇒ extremelyshort-wavelength ⇒ high-frequency transmissionsrelatively complex circuits

Lasersignificantly lower power consumption (cheap, narrow beamsemitted)free line-of-sight paths requiredpassive (no power) optical transmission techniques(mirror-based retroreflection)


Applications (I)

Sensors for a wide variety of conditions:

temperature

motion

noise levels

light conditions

humidity

object presence

mechanical stress levels on attached objects


Applications (II)

continuous sensing

detection of a crucial event

location sensing (including motion tracking)

local control of actuators (ambient intelligence)

micro-sensing


Applications (III)

Environmental applicationsforest fire detectionflood detectionprecision agricultureweather forecastplanetary exploration

Health applicationstelemonitoring of human physiological datatracking and monitoring doctors/patients


Applications (IV)

Home applicationshome automation (local/remote management of home devices)smart environments (adapt to the people’s needs)

More applications (partial list)inventory controlsecurity / militaryvehicle tracking and detectioninteractive museummonitoring material fatigue


Emerging Trends (I)

The Internet of Things (IoT)A vision: the Digital and Physical Worlds converge

Pervasive Technologies: “The most profound technologies are those thatdisappear. They weave themselves into the fabric of everyday life until they areindistinguishable from it” (Mark Weiser)Embedded Sensor Networks: “Google for the Physical World” (Deborah Estrin)

IoT: massive, seamless, wireless inclusion of everyday objects (clothes,vehicles, glasses etc.) and devices (lights, HVAC, refrigerator etc.) into theWEB, so that they become “smart” and can participate into smart scenaria andservices.


Emerging Trends (II)

Mobile Sensing/Crowdsensing

Current smartphones are truly mobile andinclude several sensors (light, accelerometer,gyroscope, proximity etc.).

Sensory readings can be obtained fromeverywhere and all the time, from the“crowd”.

Also, these sensor hints can be used toextract human features (e.g. whethersomebody is walking or lying etc.).

The above sensor readings and hints can beexploited to optimize smart scenaria andservices, in view of this feedback from thecrowd (crowdsourcing).


Sensors vs Ad-hoc Wireless Networks

much larger number of devices

more dense deployment / interactions / complexity

frequent failures

more limited capabilities

more frequently/more dynamically changing topology

less global knowledge


New/Critical Challenges (I)

Scalabilityhuge number of sensor nodeshigh densities of nodesmany complex interactions

Success rate: percentage of delivered dataHow does protocol performance scale with size?Even correctness may be affected by size.


New/Critical Challenges (II)

Fault-tolerance

Sensors mayfail (temporarily or permanently)be blocked / removedcease communication

due to various reasonsphysical damagepower exhaustioninterferencepower saving mechanisms

Can the network tolerate failures well?


New/Critical Challenges (III)

Efficiencyenergy spenttime (for data propagation)


New/Critical Challenges (IV)

Inherent trade-offs (e.g. energy vs time)Competing goals / diverse aspects:

- minimizing total energy spent in the network- maximizing the number of “alive” sensors over time- combining energy efficiency and fault-tolerance- balancing the energy dissipation

Application dependence

Dynamic changes / heterogeneity

⇓variety of protocols needed / hybrid combinations

adaptive protocols, locality

simplicity, randomization, distributedness


C. Thematic structure of the course

Introduction /Data propagation in Wireless Sensor Networks I

Data propagation in Wireless Sensor Networks II (probabilisticrouting)

Energy balance and optimization

Network deployment, connectivity, coverage

Geographic routing and obstacle avoidance algorithms

Distributed autonomous robots

Mobile Robotic Sensor Networks

Bluetooth Networks

Radio Networks

Dominating set based routing


D. A “canonical” problem: Local Event Detection and DataPropagation

A single sensor, p, senses a local event E . The general propagationproblem is the following:

“How can sensor p, via cooperation with the rest of thesensors in the network, propagate information about eventE to the control center?”


E. Representative data propagation protocols

Directed Diffusion (DD): a tree-structure protocol (suitable forlow dynamics)

LEACH: clustering (suitable for small area networks)

Local Target Protocol (LTP): local optimization (best for densenetworks)

Probabilistic Forwarding Protocol (PFR): redundant optimizedtransmissions (good efficiency / fault-tolerance trade-offs, bestfor sparse networks)

Energy Balanced Protocol (EBP): guaranteeing same per sensorenergy (prolong network life-time)


E1. Directed Diffusion

requires some coordination between sensors

creates / maintains some global structure (set of paths)

a paradigm / suite of several protocols

here: a tree-based version


Directed Diffusion elements

Interest messages (issued by the control center)

Gradients (towards the control center)

Data messages (by the relevant sensors)

Reinforcements of gradients (to select “best” paths)


Interests

An interest contains the description of a sensing task.Task descriptions are named e.g. by a list of attribute-valuepairs.The description specifies an interest for data matching theattributes.

type = wheeled vehicle // detect vehicle locationinterval = 10 ms // send events every 10 msduration = 10 minutes // for the next 10 minutesrect = [-100, 100, 200, 400] // from sensors within rectangle

Example of an interest

Interests are injected into the network at the control center


Gradients

For each interest message received, a gradient is created

Gradients are formed by local interaction of neighboring nodes,establishing a gradient towards each other.

Gradients store a value (data rate) and a direction (towards the sink)for “pulling down” data.


Data propagation

A sensor that receives an interest it can serve, begins sensing.

As soon as a matching event is detected, data messages are sentto the relevant neighbors using the gradients established.

A data cache is maintained at each sensor recording recenthistory.


Reinforcement for Path Establishment

The sink initialy repeatedly diffuses an interest for a low-rate eventnotification, through exploratory messages.

The gradients created by exploratory messages are called exploratoryand have low data rate.

As soon as a matching event is detected, exploratory events aregenerated and routed back to the sink.

After the sink receives those exploratory events, it reinforces one (ormore) particular neighbor in order to “draw down” real data.

The gradients that are set up for receiving high data rate informationare called data gradients.


Path Establishment Using Positive Reinforcement

To reinforce a neighbor, the sink re-sends the original interestmessage with a higher rate.

Upon reception of this message a node updates the correspondinggradient to match the requested data rate.

The selection of a neighbor for reinforcement is based on localcriteria, i.e.:

the neighbor that reported first a new event is reinforced.

the higher data rate neighbor is reinforced.

more than one neighbors are reinforced.

The data cache is used to determine which criteria are fulfilled.


Negative Reinforcement

Negative reinforcement is applied when certain criteria are met i.e. agradient does not deliver any new messages for an amount of time, ora gradient has a very low data rate, etc.


Summary Evaluation of Directed Diffusion

improves over flooding a lot

significant energy savings

performance drops when network dynamics are high


E2. Low Energy Adaptive Clustering Hierarchy (LEACH)

The network is partitioned in clusters.

Each cluster has one cluster-head.

Each non cluster-head sends data to the head of the cluster itbelongs to.

Cluster-heads gather the sent data, compress it and send it to thesink directly.


Dynamic Clusters


Randomized rotation of cluster-heads

Node n decides with probability T(n) to elect itself cluster-headP: the desired percentage of cluster heads.r: the current round.G: the set of nodes that have not been cluster-heads in the last 1

Prounds.

T(n) is chosen so as to get on the average the same number ofcluster-heads in each round.

T(n) =

{P

1−P∗(rmod 1P )

if n ∈ G

0 otherwise


Cluster-head advertisement

Each cluster-head broadcasts an advertisement message to therest nodes using a CSMA-MAC protocol.

Non cluster-head nodes hear the advertisements of allcluster-head nodes.

Each non cluster-head node decides its cluster-head by choosingthe one that has the stronger signal.


Cluster set-up phase

Each cluster-head is informed for the members of its cluster.

The cluster-head creates a TDMA schedule

The cluster-head broadcasts the schedule back to the clustermembers.


Steady Phase

Non cluster-headsSense the environment.Send their data to the cluster head during their transmission time.

Cluster-headsReceive data from non cluster-head nodes.Compress the data received.Send their data directly to the base station.


Summary Evaluation of LEACH

reduces energy dissipation through compression of data atcluster-heads.

in large networks, direct transmissions are very expensive.

performance drops when the network traffic is high (e.g. manyevents generated).


Papers related to this Lecture

W.R. Heinzelman, A. Chandrakasan & H. Balakrishnan,“Energy-Efficient Communication Protocol for WirelessMicrosensor Networks”, 33rd Hawaii International Conferenceon System Sciences, HICCS-2000.

C. Intanagonwiwat, R. Govindan, & D. Estrin, “DirectedDiffusion: A Scalable and Robust Communication Paradigm forSensor Networks”, 6th Annual International Conference onMobile Computing and Networking (MobiCOM ’00), August2000.


State of the art Surveys

- Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., and Cayirci, E.:Wireless Sensor Networks: a Survey. In the Journal of ComputerNetworks 38 (2002) 393-422.

- Estrin, D., Govindan, R., Heidemann, J., and Kumar, S.: NextCentury Challenges: Scalable Coordination in Sensor Networks.In Proc. 5th ACM/IEEE International Conference on MobileComputing (MOBICOM), 1999.

- Kahn, J.M., Katz, R.H., and Pister, K.S.J.: Next CenturyChallenges: Mobile Networking for Smart Dust. In Proc. 5thACM/IEEE International Conference on Mobile Computing(September 1999) 271-278.


Books (I)

F. Zhao and L. Guibas, Wireless Sensor Networks: AnInformation Processing Approach, Morgan Kaufmann, 2004.

C. Raghavendra and K.Sivalingam (Editors), Wireless SensorNetworks, Springer Verlag, 2006.

Bhaskar Krishnamachari, Networking Wireless Sensors,Cambridge University Press, December 2005.


Books (II)

S. Nikoletseas and J. Rolim, Theoretical Aspects of DistributedComputing in Sensor Networks, Springer Verlag, 2011.


Book Themes

T1. Challenges for Wireless Sensor NetworksT2. Models, Topology, ConnectivityT3. Localization, Time Synchronization, CoordinationT4. Data Propagation and CollectionT5. Energy OptimizationT6. Mobility ManagementT7. Security AspectsT8. Tools, Applications and Use Cases


Books (III)

S. Nikoletseas, Y. Yang and A. Georgiadis, Wireless PowerTransfer Algorithms, Technologies and Applications in AdHoc Communication Networks, Springer Verlag, 2016.


Book Themes

T1. TechnologiesT2. CommunicationT3. MobilityT4. Energy FlowT5. Joint OperationsT6. Electromagnetic Radiation Awareness


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