Lecture XVI: Mobile and Ubiquitous Computing

1. Lecture XVI: Mobile and Ubiquitous Computing CMPT 401 2008 Dr. Alexandra Fedorova

2. 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) • Computers adapt to context, integrated into your life

3. 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) • Wearable health monitors collect health data normally measured by doctors/nurses • Eliminates the need for doctor visits – sensors can alert of dangerous health conditions • Context-sensitive personal assistant

4. Weather Toaster • Created by an industrial design student in Brunel University, London • You wake up in the morning • Go make a toast • The toast pops out • With the weather forecast shown on it…

5. Weather Toaster • Your toast tells you if it’s • Sun • Clouds • Rain • Modem inside the toaster • Dials up to the Internet over a free phone service • Connects to the weather forecast site • Uses Java to parse the data • Convert it into a number • Burn on the toast using a heat-resistant stencil

6. 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)?

7. 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? • How do we mine it? • How do we make sure it’s safe and secure?

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

9. 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

10. 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

11. Existing PdM Technologies: Manual Data Collection Data is collected into a hand-held device 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

12. Existing PdM Technologies: Online Surveillance Data acquisition unit Sensor Central repository Sensors are connected to equipment, hardwired to data acquisition unit Data acquisition unit processes the data and delivers it across a wired network to a central repository

13. 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)

14. 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

15. 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

16. 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 dynamics, wear patterns • Compare data to standard change rates, project equipment lifetime

17. 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?

18. 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 the monitored 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

19. 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

20. 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

21. 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)

22. 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

23. Hardware Architecture Sensor node (Mica2 mote) • 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

24. 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

25. 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

26. 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

27. Sleep/Wakeup Schedule • Sensor 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

28. 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 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

29. Fault Tolerance • Sensor networks must operate in harsh environments for long periods of time • Failures are common and should be expected

30. Fault Tolerant Design • Four design features to increase fault tolerance: • Watchdog timers – a node resets itself upon encountering unexpected behavior: radio lockup, too much time between packets, protocol violations, etc. • 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

31. 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 energy • 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

32. 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