Lecture XVI: Mobile and Ubiquitous Computing

CMPT 401 Summer 2007

Dr. Alexandra Fedorova

Lecture XVI: Mobile and Ubiquitous Computing

2CMPT 401 Summer 2007 © A. Fedorova

Mobile and Ubiquitous Computing

• Mobile computing – computers that users can carry – Laptops, handhelds, cell phones– Wearable computers

• Heart monitors used by athletes (Tour de France: team manager monitors heart rates, give recommendations on tactics)

• Health monitors used by elderly

• Ubiquitous computing– Computers are everywhere– Each person uses more than one computer– PC, laptop, cell phone, watch, car computer (100+

microprocessors in some cars)


Enables New Cool Applications

• Object tracking– Track location of a child, parent, dog, car (lojack)– Parents watch their babies in the daycare

• Health monitoring– Monitor child breathing (prevent SIDS – sudden infant death syndrome)– Heart stimulation: embed hearth sensors in the elderly. If pulse goes too low,

stimulate the pulse• Replace physicians visits (Neuromancer project at Sun Microsystems, Jim

Waldo)– People wear health monitors– They collect health data normally measured by doctors/nurses– Eliminates the need for doctor visits – sensors can alert of dangerous health

conditions– Massive data available – a chance to carry out longitudinal studies in medicine


Some Challenges

• Limited power– Wearable devices and sensors have low battery power– To be interesting, sensors must transmit data– Data transmission uses power– How to minimize power consumption and maximize transmission of

useful data?• Limited network bandwidth

– Applications must communicate to sensors exactly what data they need, so sensors don’t transmit useless data

• Limited connectivity– Mobile devices often operate in disconnected mode– How to associate to a new network seamlessly?– How to form a network without an infrastructure (ad-hoc networking)?


More Challenges

• Sensor deployment– Sensors have limited lifetime, at some point they become useless – In ecologically sensitive environments – this means a bunch of silicon

scattered around– Example: deploy sensors for forest fire detection. Scatter sensors around

the forest (from a helicopter)– After a while you have a whole lot of improperly disposed batteries

• Handling data– Once all these super-apps get implemented, we’ll have massive amounts of data

collected by all imaginable sensors– Much of this data will be kept around for historical analysis– Where do we store this data? (P2P? – addressed by Neuromancer)– How do we make sure it’s safe (replication?)– How do we make sure it’s secure?


Case Studies of Sensor Networks

• Design and Deployment of Industrial Sensor Networks: Experiences from a Semiconductor Plant and the North Sea, Krishnamurthy et al.

• IrisNet: An Architecture for a Worldwide Sensor Web, Gibbons et al.


Industrial Sensor Networks• Sensor networks used for predictive equipment maintenance

– Monitor industrial equipment– Detect oncoming failures– Alert humans of potential failures

• We will talk about– Motivation– System architecture– System issues specific to wireless sensor networks

• Two case studies– Semiconductor fabrication plan– Oil tanker in the North Sea


Predictive Equipment Maintenance (PdM)

• Monitor and assess the health status of a piece of equipment (e.g., a motor, chiller, or cooler)

• PdM allows to detect most failures in advance• But analysis has to be performed with sufficient frequency• Equipment has sensors attached to it• Sensors monitor conditions of the equipment• Report results to the operator’s computer• Operator analyses data, detects any unusual patterns,

decides if failure is imminent• Takes action to replace the equipment


Types of Sensor Data• Vibration (used in this study) – analyze frequency and amplitude of vibrations

over time– Identify unexpected changes – suggest repair or replacement– Source of vibrations must be identified and assigned to a specific component

• Oil analysis – analysis of wear particles, viscosity, acidity and raw elements– Capture a small sample, compare to baseline samples – detect potential

problems• Infrared Thermography – sense heat at frequencies below visible light

– Detect abnormal heat sources, cold areas, liquid levels in vessels, escaping gases

• Ultrasonic detection – detect wall thickness, corrosion, erosion, flow dynaics, wear patterns

– Compare data to standard change rates, project equipment lifeime


Importance of PdM

• Reduce catastrophic equipment failures• Save human lives• Reduce associated repair and replacement cost• Save money – switch from calendar-based maintenance to

indicator driven maintenance– Calendar-based maintenance: may do maintenance when you don’t

need to – May fail to do the maintenance when you really have to

• Quantify the value of a new system within the warranty period

• Meet factory uptime and reliability requirements


Existing PdM Technologies: Manual Data Collection

A human operator visits the equipment under surveillance

Sensors are installed in the equipment or brought by the operator

Data is transported to the lab for analysis

Data is collected into a hand-held device


Existing PdM Technologies: Online Surveillance

Sensors are connected to equipment, hardwired to data acquisition unitData acquisition unit processes the data and delivers it across a wired network to a central repository

SensorData acquisition

unit

Central repository


Disadvantages of Existing Technologies

• Manual data collection:– Potential for user error– High cost to train and keep experts– Cost of manpower for frequent data collection– Most users of manual data collection are not happy with the level

of prediction and correlation• Online systems:

– Cost of hardware and network infrastructure– Only appropriate for equipment with cost impact of over $250K in

case of failure– Online systems are used in only 10% of the market (due to cost)


Wireless Sensor Networks for PdM

• Provide frequency of monitoring comparable to online systems

• Lower cost of deployment – network is wireless– Just drop the sensors and you are ready to go

• Data acquisition unit needs not be specialized hardware– Just any computer that can listen for radio signals from sensors


Challenges in Deployment of Wireless Sensor Networks

• Determine requirements for industrial environments:– How often does data need to be sampled? – In what form to transmit and organize the data?– How long will the sensor battery survive?

• Effect of environment on deployment– What is the signal quality in the current environment? Lots of thick

walls is bad for the signal– How often will the network be disconnected – i.e., in the ship the

compartment containing sensors is periodically shut off• How to ensure the required quality

– Sensors will fail, how do you ensure that sufficient data collection rates are achieved?


Setup for Vibration Analysis

• Accelerometer – a device used to measure vibrations or accelerations due to gravity change or inclination

• Measures its own acceleration, so it must be hard-mounted to themonitored equipment

• In the experiment, an off-the-shelf accelerometer was used; it interfaces with the rest of the sensor board (radio, etc.)

• Sensor network interfaces with an off-the-shelf software application – provides long term data storage, trend analysis, fault alarms


Site Planning

• How/where to install the sensors given the particularities of a given site?

• Sensors must be safe for the equipment they monitor• Radio Frequency (RF) coverage – are there walls and

equipment preventing good RF coverage? Must relay nodes or gateways be installed?

• RF interference – is there RF noise that will prevent good transmission? RF interference may come from other radios used on the site.

• To assess these factors, a site survey is needed


Site Survey

• Place test sensors near sensing points (where actual sensors will be mounted in the future)

• Place test gateways (the machines that will receive data from sensors and transmit it further) at locations where actual gateways would be placed– Near power outlets and Ethernet jacks

• Using test setup, evaluate wireless connectivity, RF coverage and interference


Site Survey Results• Sensor nodes with more powerful radios worked better in

conditions with RF interference• Less powerful radios were not able to transmit through a

door on the oil tanker• It had to be ensured that sensor node frequencies did not

overlap with critical radio frequencies used on the oil tanker• Witnessed better RF performance on the oil tanker than was

initially expected:– Attributed to use of steel materials on the ship– Steel materials reflect, rather than attenuate RF energy (unlike office

and home environments)


Application Specific Requirements

• Data must be accurate, acquired and transmitted in a timely manner– Challenge: sensors and data acquisition units will fail due to

operation in a harsh environment– Solution: system must be designed with expectation for failure

and with ability to quickly recover from failures• Long-lived battery powered operation

– Sensor networks should not use plant power – Should be battery operated: must operate for a long time on one

set of batteries, to avoid the need for frequent redeployment


Hardware Architecture

• Two types of sensor nodes :– Mica2 Mote– Intel Mote

• Mote:– Composed of a small, low

powered computer– Radio transmitter– Connected to several

sensors• The node’s sensor board is

connected to vibration sensors

Sensor node (Mica2 mote)


Hardware Architecture Comparison

• Mica2– Less powerful radio– No on-board storage for sensor data, so you need to attach

additional storage to it• Intel

– Very powerful radio: 10x throughput of the Mica2 mote– Uses more power


Network Architecture

• Hierarchical architecture– Sensor clusters (sensor

mesh)– Cluster head (connected to

the gateway)– Stargate Gateway

• mote radio• 802.11 radio

– 802.11 backbone– Root Stargate– Bridge Stargate– Enterprise server


Data Collection and Transfer

• Cluster head schedules data capture/transfer for every sensor connected to each node

• When a node has captured data it initiates a connection to the Stargate gateway

• Data is transferred using a reliable transport protocol• Sensor data is time-stamped and put in a file• There is a separate file for each collection of a sensor channel• Each Stargate gateway periodically copies file to the root gateway• Root gateway transfers data to Bridge gateway via serial cable – this

is done to isolate wireless network from the corporate network• Bridge gateway transfers data to the enterprise server


Hierarchical Network Structure

• Tier 1 – lowest level– Networks of sensor nodes– They form clusters: may be pre-assigned to a cluster or choose the cluster

dynamically– Lowest compute capability, limitations on bandwidth and battery capaciry

• Tier 2 – middle level– Sensor network backbone– Individual cluster gateways– Higher compute and power capacity – offload computational burden from

Tier 1• Tier 3 – highest level

– Interface to the enterprise– Abstracts application needs from the sensor network


Sleep/Wakeup Schedule

• Sensor nodes form a cluster around a gateway• Nodes in a cluster follow a sleep/wakeup protocol• When nodes wake up they acquire data from sensors and

transmit it to the gateway• Then they go to sleep until the next data collection is

scheduled• Sleep/wake-up operation saves battery power• Sleep/wake-up schedule is coordinated by a cluster head

– a device connected to the gateway via a serial port


Power Management Protocol

• Cluster head schedules sleep periods based on application-level sampling requirement

• Upon initial discovery of nodes in the cluster, cluster head sends the first request for data collection

• At the end of each data collection it sends a message indicating start time and duration of next sleep phase

• Sensor nodes go to sleep and then wake up all together• When nodes are asleep they are not completely turned off, but they

operate in a low power mode• Nodes’ clocks are not perfectly synchronized, so the cluster head

waits for some “skew” period until beginning next data collection• Sleep periods in the oil tanker installation were set to 7 and 18 hours


Fault Tolerance

• Sensor networks must operate in harsh environments for long periods of time

• Failures are common and should be expected


Fault Tolerant Design

• Four design features to increase fault tolerance:– Watchdog timers – a node resets itself upon encountering

unexpected behavior– Cluster heads store network state – nodes can return to operation

quickly after being reset– Intentional re-initialization of sensor nodes after each collection

period– Non-volatile storage of critical state at cluster head – cluster head

could be (and was) reset after each wake-up period


Watchdog Timers

• Each node monitors events:– How much time has passed since last packet reception (in the

wake state)– Events signifying radio lockups– Protocol events – e.g., receipt of new data send request before

the previous one was finished• The node resets itself if any of these unexpected events

was detected


Comparing Power Consumption

• Active power – power when the network is awake– Similar usage of active power per unit of time– But Intel motes spent less time being awake, because they had faster radios– So Intel-based network used less power overall

• Power during the sleep phase– Intel network implemented a connected sleep mode– You can still access the network while the nodes are asleep, albeit at a

higher latency– So it used more power in the sleep mode– If Intel-based network were completely disconnected, it would use only

slightly more power as Mica2-based network– Using an external real-time clock can enable completely turning off the

network during the sleep mode – even more power would be saved


Battery Life

• On the oil tanker, two lengths of sleep mode were used:– 18 hour sleep period– 5 hour sleep period

• Resultant battery lives are:– 18-hour period: 82 days– 5-hour period: 21 days


Case Studies of Sensor Networks

• Design and Deployment of Industrial Sensor Networks: Experiences from a Semiconductor Plant and the North Sea, Krishnamurthy et al.

• IrisNet: An Architecture for a Worldwide Sensor Web, Gibbons et al.


IrisNet

• A slightly different environment than conventional sensor networks

• Many devices: PCs, hand-helds, cameras• Good connectivity, no power limitations• Provide useful data• Question:

– How do we access and integrate this data to enable interesting applications?

• Solution: – Architecture for a Worldwide Sensor Web


IrisNet Vision

• A user will query, as a single unit, vast quantities of data from thousands of widely distributed sensors

• Many possible uses:– Epidemic Early Warning System - monitor water quality, oil spills– Homeland Security– Computer Network Monitoring – gather (sense) data on

bandwidth/CPU usage; answer queries such as “What’s the least loaded node at SFU?”

– Traffic / Parking Assistance – help me find hockey game parking in Vancouver


IrisNet Goals• Planet-wide local data collection and storage

– Massive amounts of data– Retain data near its source, transmit to the Internet only as needed

• Ease of service authorship– Vision: when sensors are deployed, we don’t know all potential users– Different service providers might want to collect different data and different rates

and apply different filters depending on the service• Real-time adaptation of collection and processing

– Reconfigure data collection and data filtering processes, change sampling rates• Data as a single queriable unit

– Global sensing device network is a single unit that supports a high-level query language

– Users make complex queries: “Tell me the location of my grandmother at the time when the oil spill in the Baltic sea was first detected”.

• Data integrity and privacy– No one should be able to query my health data except my doctor


IrisNet Architecture

User

. . .Sensor

SA

senseletsenselet

Sensor

SA

senseletsenselet

Sensor Sensor

SA

senseletsenselet

Web Serverfor the url

. . .Query

OAXML database

. . .OA

XML databaseOA

XML database

Two components:SAs: sensor feed processingOAs: distributed database

From slides of P. Gibbons


IrisNet Architecture

• Sensing Agents (SA)– Generic data acquisition interface: ask sensor to collect data X at

frequency Y, filter data according to parameter Z– A service configures sensing agent according to its needs– Configuration is done via execution of service-specific code

senslet– A single SA can execute one or more senslets

• Organizing Agents (OA)– Service specific sensing data is stored in a database– This database is queried by users


Organization of SA


OA Architecture

• XML-based database• Hard to design rich schema

for all possible service• XML allows the use of self-

describing tags• Database is partitioned and

distributed• Replicate parts of the

database• Primary replicas: strong

consistency• Secondary replicas: weak

consistency


Querying the IrisNet

• Each node has a human readable name• Each such name is registered in the DNS with associated IP address• Query is routed to the IP address

neighbourhood-Shadyside.

city-Pittsburgh.state-PA.

usRegion-NE.intel-iris.net


Example Services

• Parking space finder– Uses cameras throughout a metropolitan area to track parking space availability– Users fill out a Web form to specify destination and any constraints on a desired

parking space– Parking space finder identifies the parking space satisfying constraints

• Network and host monitor (IrisLog)– Collects data from computer and network monitoring tools– Those tools act like sensors– They report data, such as CPU and memory load, network bandwidth– Answer queries such as “find the least loaded node on the network”

• Coastal imaging service– Uses camera installed at Oregon coastline– Uses live feed from cameras to identify signatures of phenomena such as riptides

and sandbar formations


Summary

• Variety and quantity of small computers is exploding• These computers are mobile, wearable, provide a variety of cool

functions/sensing abilities, and are affordable!• One can imagine a multitude of useful “killer apps” using those

devices• Many challenges need to be overcome to make these applications

really work:– Limited power and network bandwidth– Formation of ad-hoc networks– Querying the available data– Handling and storing massive amounts of data

Lecture XVI: Mobile and Ubiquitous Computing

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