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CMPT 401 Summer 2007 Dr. Alexandra Fedorova Lecture XVI: Mobile and Ubiquitous Computing
2 CMPT 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)
3 CMPT 401 Summer 2007 © A. Fedorova 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
4 CMPT 401 Summer 2007 © A. Fedorova 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)?
5 CMPT 401 Summer 2007 © A. Fedorova 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?
6 CMPT 401 Summer 2007 © A. Fedorova 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.
7 CMPT 401 Summer 2007 © A. Fedorova 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
8 CMPT 401 Summer 2007 © A. Fedorova 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
9 CMPT 401 Summer 2007 © A. Fedorova 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 – a nalysis of wear particles, viscosity, acidity and raw elements –Capture a small sample, compare to baseline samples – detect potential problems Infrared Thermography – s ense 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
10 CMPT 401 Summer 2007 © A. Fedorova 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
11 CMPT 401 Summer 2007 © A. Fedorova 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
12 CMPT 401 Summer 2007 © A. Fedorova Existing PdM Technologies: Online Surveillance 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 Sensor Data acquisition unit Central repository
13 CMPT 401 Summer 2007 © A. Fedorova 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 CMPT 401 Summer 2007 © A. Fedorova 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
15 CMPT 401 Summer 2007 © A. Fedorova 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?
16 CMPT 401 Summer 2007 © A. Fedorova 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
17 CMPT 401 Summer 2007 © A. Fedorova 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
18 CMPT 401 Summer 2007 © A. Fedorova 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
19 CMPT 401 Summer 2007 © A. Fedorova 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)
20 CMPT 401 Summer 2007 © A. Fedorova 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
21 CMPT 401 Summer 2007 © A. Fedorova 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)
22 CMPT 401 Summer 2007 © A. Fedorova 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
23 CMPT 401 Summer 2007 © A. Fedorova 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
24 CMPT 401 Summer 2007 © A. Fedorova 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
25 CMPT 401 Summer 2007 © A. Fedorova 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
26 CMPT 401 Summer 2007 © A. Fedorova 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
27 CMPT 401 Summer 2007 © A. Fedorova 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
28 CMPT 401 Summer 2007 © A. Fedorova Fault Tolerance Sensor networks must operate in harsh environments for long periods of time Failures are common and should be expected
29 CMPT 401 Summer 2007 © A. Fedorova 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
30 CMPT 401 Summer 2007 © A. Fedorova 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
31 CMPT 401 Summer 2007 © A. Fedorova 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
32 CMPT 401 Summer 2007 © A. Fedorova 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
33 CMPT 401 Summer 2007 © A. Fedorova 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.
34 CMPT 401 Summer 2007 © A. Fedorova 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
35 CMPT 401 Summer 2007 © A. Fedorova 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
36 CMPT 401 Summer 2007 © A. Fedorova 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
37 CMPT 401 Summer 2007 © A. Fedorova IrisNet Architecture User... Sensor SA senselet Sensor SA senselet Sensor SA senselet Web Server for the url... Query OA XML database... OA XML database OA XML database Two components: SAs: sensor feed processing OAs: distributed database From slides of P. Gibbons
38 CMPT 401 Summer 2007 © A. Fedorova 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
39 CMPT 401 Summer 2007 © A. Fedorova Organization of SA
40 CMPT 401 Summer 2007 © A. Fedorova 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
41 CMPT 401 Summer 2007 © A. Fedorova 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
42 CMPT 401 Summer 2007 © A. Fedorova 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
43 CMPT 401 Summer 2007 © A. Fedorova 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
CMPT 431 Dr. Alexandra Fedorova Lecture XVI: Mobile and Ubiquitous Computing.
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