Making Smart
             Phones Smarter
             Four mobile s...
Related Work in Mobile Sensing

   R       esearch progress in wireless and sensor networking in
           the past decad...

                                                                                           technology ...
   Carbon monoxide (ppm)





                                   pollution               A3 (Q22). I have walked

     revealed a range of research challenges   the complex data-processing tasks to        the phone a...
also hoping to develop efficient veri-                                                                                    ...
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Mobsens -Journal paper


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MobSens: Making Smart Phones Smarter

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Mobsens -Journal paper

  1. 1. CONTENT SHARING MobSens: Making Smart Phones Smarter Four mobile sensing applications that work on off-the-shelf mobile phones contain elements of health, social, and environmental sensing at both individual and community levels. T oday’s mobile phones are smarter MobSens Prototypes than ever: they now take and pro- Mobile sensing—also known as “participatory cess pictures and videos, issue sensing,”2 “urban sensing,”3 or “participatory messages and email, access the urbanism”4 —enables data collection from Web, allow games on demand, large numbers of people in ways that weren’t and play music. More people around the world previously possible. Using mobile phones has take their phones everywhere they go, using several advantages over unattended wireless them in a variety of environments and situations sensor networks for environmental sensing to perform a whole range of different tasks. applications: In India, for example, more people access the Internet from their phones than from a PC, a • Mobile phones can provide coverage where scenario that will certainly play out across the static sensors are hard to deploy and main- globe in the years to come.1 tain, and large numbers of cell phones already Most mobile phones include a variety of sens- exist around the world, providing the physical ing components. By expanding this capability, sensing infrastructure. we can derive some interesting • Deploying the sensing hardware and pro- Eiman Kanjo, Jean Bacon, sensing modalities—for ex- viding it with network and power requires and Peter Landshoff ample, scrutinizing local envi- significant effort in other sensor network- University of Cambridge ronments to detect and reduce ing systems. The availability of more pow- pollution or using medical ap- erful operating systems and the transfer David Roberts plications to tackle other prob- of standardized programming languages Nokia lems on a societal scale. on ever-smaller computing platforms have In this article, we dis- spurred the recent development of software cuss experiences and lessons applications for mobile computers, includ- learned from deploying four mobile sensing ing Symbian (, Google applications on off-the-shelf mobile phones Android (, within a recreational framework called Mob- Microsoft Mobile ( Sens that contains elements of health, social, windowsmobile), and iPhone ( and environmental sensing at both individual com/iphone). and community levels. We describe the main • Such systems can benefit from local com- components of our applications, which facili- munities as the driving element for environ- tate logging and external communications. We mental sensing. This approach, sometimes also outline the challenges faced when building referred to as “citizen science,” uses mobile and testing these applications and describe our sensor technology to help individuals person- strategies for overcoming them. ally collect, share, compare, and participate 50 PER VA SI V E computing Published by the IEEE CS n 1536-1268/09/$26.00 © 2009 IEEE
  2. 2. Related Work in Mobile Sensing R esearch progress in wireless and sensor networking in the past decade has been astounding; recent develop- ments in sensor networks in which the nodes are mobile and will house several optional sensors to monitor environment, health, and local weather conditions on a dedicated mobile phone. carried by people or vehicles have also emerged.1,2 In particu- lar, industry leaders are making some headway into chang- ing the mobile sensing paradigm. For example, SensorPlanet REFERENCES ( is a Nokia-initiated global research 1. B. Hull et al., “CarTel: A Distributed Mobile Sensor Computing Sys- framework for mobile device-centric wireless sensor networks tem,” Proc. 4th ACM Conf. Embedded Network Sensor Systems (SenSys that views mobile devices as both gateways to mesh sensor net- 06), ACM, 2006; works and as sensor nodes themselves.3,4 2. S.B. Eisenman et al., “MetroSense Project: People-Centric Sensing at Various projects5–7 have shown how the integration of sen- Scale,” Proc. Workshop World-Sensor-Web (WSW), ACM Press, 2006; sors and positioning technologies used in conjunction with mobile computing devices can support data collection for 3. V.H. Tuulos, J. Scheible, and H. Nyholm, “Combining Web, Mo- environmental applications, without the overheads and com- bile Phones and Public Displays in Large-Scale: Manhattan Story plexities of wireless sensor networks. Some of these projects Mashup,” Proc. 5th Int’l Conf. Pervasive Computing, LNCS 4480, have considered the embedding of direct awareness in mo- Springer, 2007, pp. 37–54. bile devices, which is boosted by the rapid advance in sensor 4. T. Abdelzaher et al., “Mobiscopes for Human Spaces,” IEEE Pervasive technology. Computing, vol. 6, no. 2, 2007, pp. 20–29. MobSens builds on a large body of related projects that use 5. E. Kanjo et al., “MobGeoSen: Facilitating Personal Geosensor Data mobile phones as sensing devices. The MetroSense project, Collection and Visualization Using Mobile Phones,” Personal and for example, outlines a “people centric” approach to mo- Ubiquitous Computing, vol. 12, no. 8, 2008, pp. 599–607; http:// bile phone sensing that includes several deployments with bicycles.8 6. A. Steed and R. Milton, “Using Tracked Mobile Sensors to Make The Center for Embedded Network Sensing has a research Maps of Environmental Effects,” Personal and Ubiquitous Computing, initiative called “participatory sensing” that’s developing the vol. 12, no. 4, 2008, pp. 331–342. infrastructure and tools to let individuals and groups initiate 7. P. Adamczyk et al., “Urban Computing and Devices,” IEEE Distributed their own public “campaigns” for others to participate in by Systems Online, 2007; using networked mobile devices.9 The MyExperience tool is a MDSO.2007.46. mobile software application for in situ data collection that sup- 8. A. Campbell et al., “PeopleCentric Urban Sensing,” Proc. 2nd ACM/ ports the study of human behavior and the evolution of mobile IEEE Ann. Int’l Wireless Internet Conf. (WICON 06), ACM Press, 2006; computing technologies.10 MyExperience can also record a wide range of data, including information from sensors, im- 9. J. Burke et al., “Participatory Sensing,” Proc. World Sensor Web Work- ages, video, audio, and user surveys. shop, ACM Press, 2006; The Metro project allows mobility-enabled interactions wsw06_ucla_final.pdf. between human-carried mobile sensors via static sensors em- 10. J. Froehlich et al., “MyExperience: A System for In Situ Tracing and bedded in the civic infrastructure and wireless access nodes Capturing of User Feedback on Mobile Phones,” Proc. MobiSys 2007, that provide a gateway to the Internet.11 Finally, Nokia has ACM Press, 2007; introduced a new phone concept called “Nokia Eco Sen- 11. A.T. Campbell et al., “The Rise of People-Centric Sensing,” IEEE Inter- sor” ( Its wearable sensor unit net Computing, vol. 12, no. 4, 2008, pp. 30–39. in interpreting the personal measure- data (such as photos and messages), qualitative environment information ments of their daily lives. which can be coupled with sensor directly from their surroundings. In • Mobile phones can directly pick up data. the long run, this increased information sensor data instead of having to send could also promote “green” options not that data across an entire sensor Essentially, our mobile sensing proj- just in city streets but also in other as- network. ect aims to equip average citizens with pects of daily life. • Mobile phones can acquire, pro- mobile phone applications and tools A variety of prototypes have emerged cess, store, and transfer contextual that let them acquire quantitative and from our research to help a mobile OCTOBER–DECEMBER 2009 PER VA SI V E computing 51
  3. 3. CONTENT SHARING technology for on-device data gather- ing, refining, integrating, and interpret- ing. They can also transfer data to and from remote servers linked to a broad range of resources. We hope our mo- bile system will have a direct impact on respiratory health and indirect impacts on healthy user habits, such as encour- aging more walking and cycling. Mobile Experiences We ultimately want a system that lets users explore data and make informed (a) (b) decisions about how they interact with their environment, but it should also enable nontechnical users to use their mobile phones without specialist knowledge in large-scale sensor data collection in real time. PollutionSpy Our PollutionSpy application aims to monitor air pollution in traffic by us- ing mobile phones to create a “pollution map” of Cambridge, England. It also promotes social networking in a local community through the provision of a Web portal that facilitates back-end sharing of real-time environmental and archived data. PollutionSpy software creates a type of Bluetooth personal network and can connect up to seven different Bluetooth devices. Within this network, the mo- bile phone serves as the master, and the other devices function as slaves. (c) So far, we’ve used this network to con- nect mobile phones to pollution sensors Figure 1. PollutionSpy. (a) One of our cycling couriers with a data logger and the for CO, NO, NO2 , CO2 , and SO2 as N95 phone ready to collect pollution data. (b) Screenshot of PollutionSpy software. well as weather sensors for tempera- (c) A 3D track of CO data near a busy junction overlaid (in real time) on aerial ture and wind speed. Upon connec- photography using Google Earth. tion, the Bluetooth devices feed sensor data tagged with locations to a log file on the phone and display this collected phone’s internal sensing devices (such • NoiseSpy, an on-device sound sensor; data graphically on the phone’s screen. as its microphone) and external wire- • Fresh, a mobile forum for commu- Users also have the option to trans- less sensors (such as off-the-shelf pol- nity environmental awareness; and fer the data to a remote database and lution sensors, health monitors, and • MobAsthma, a mobile asthma and view it in real time on our GIS mapping GPS) to collect data. We focus on four pollution monitor. tools, which are embedded in a dedi- of these prototypes here: cated Web interface. These prototype software components As Figure 1 shows, we’ve executed • PollutionSpy, a pollution monitor for provide a robust and expandable plat- several data collection campaigns re- urban areas; form for mobile sensing, including the sulting in useful data. In these ex- 52 PER VA SI V E computing
  4. 4. 8 Carbon monoxide (ppm) 6 4 2 0 19:01:36 19:01:49 19:02:00 19:02:12 19:02:24 19:02:36 19:02:46 19:02:58 19:03:10 19:03:22 19:03:32 19:03:46 19:03:56 19:04:08 19:04:19 19:04:29 19:04:41 19:04:54 19:05:03 19:05:16 19:05:27 19:05:40 19:05:52 19:06:04 19:06:15 19:06:26 19:06:38 19:06:49 19:06:59 19:07:11 19:07:23 19:07:34 19:07:46 19:07:58 19:08:09 19:08:20 19:08:30 19:08:42 19:08:54 19:09:06 Time Figure 2. Comparison of two CO sensor readings. To test how closely matched the sensors are, we had two users walk down the same side of the road with sensor boxes. periments, we focused on Nokia’s nal GPS receiver locations to generate a ambient noise, and weather events via third-generation (NSeries) N95 in a map of sound levels over the course of the information other users post; the proof-of-concept trial to demonstrate a journey. Each time the software runs interface is a mobile phone tool, so it that such monitoring techniques are on a mobile phone, it displays the noise engages and encourages seamless par- reliable and provide useful data.5 data graphically on the phone screen ticipation in real time from multiple The gas levels being measured can along with other location information, locations. Fresh could even help local change very rapidly—some users re- as Figure 3 shows. We tested our ini- communities improve their lifestyles ported that returning to the same geo- tial NoiseSpy implementation with six by providing easy access to real-time, graphical position a few minutes later cyclists over the course of two weeks. geographically measured environmen- yielded different results. To test how Initial feedback through participant tal information. closely matched the sensors are, we diaries and interviews showed that the In Fresh, the “world” is initially performed several experiments with users generally enjoyed the experience. empty, but as the interactions start, the two users carrying sensor boxes down The software is currently available on- user’s phone cell IDs fill up with ques- the same side of the road. As Figure 2 line ( tions and answers from other users shows, the results indicate a very good downloads.htm). making their way across the city. Users level of coherence. As we expected, we observed that can search their current location for any We recently developed more sensor noise levels are higher in peak periods information about the local environ- boxes that we plan to calibrate and use when roads are busy and lower in off- ment or look at tagged questions and in a large-scale experiment in Cam- peak periods, and that noise levels over answers related to that area. They can bridge. In future trials, we hope to be an area vary (for example, the Doppler then choose to answer any questions re- able to answer questions such as how effect, in which noise levels rise as a ve- lated to where they are with a short text much variation a particular area has hicle approaches and reduce again after response. If they don’t find what they’re and over what scale do changes occur. it passes), which causes short-term varia- looking for, they can start a new discus- tions in noise level. In our experiments, sion by posting a question for others to NoiseSpy one participant walked past a quiet area answer. Figure 4 shows an example sce- Sound is essential to our daily lives, but and recorded a high noise reading be- nario of three users searching for, pick- noise is not. Most people define noise cause a car passed at that point. Also, ing up, and answering environmental as sounds that are loud, annoying, and high wind caused noise levels to rise near questions about their current location in harmful to the ear, and many people junctions and open areas. Cambridge. Two of them want to know feel that traffic noise is one of the big- about ambient pollution levels. gest offenders. Fresh The Fresh prototype is still under NoiseSpy is a sound-sensing system Fresh is a mobile interface that uses development. Our aim is to both moti- that turns mobile phones into low-cost GSM networking and positioning vate users and make the user interface data loggers for monitoring environ- via the cell IDs in users’ phones to let more engaging and effective. Once the mental noise. It lets users explore a city people discuss “green” issues related interface is mature enough, we plan to area while collaboratively visualizing to their local environment. 6 In addi- release the phone software and start noise levels in real time. The software tion, users can access environmental experimenting with the system around combines sound-level data with exter- data points such as pollution levels, Cambridge. OCTOBER–DECEMBER 2009 PER VA SI V E computing 53
  5. 5. CONTENT SHARING sors and combines them with the pa- NoiseSpy tient’s current location. The system is also capable of monitoring a person’s asthma condition and remotely alert- Software developed by Dr. Eiman Kanjo University of Cambridge ing medical staff if the patient experi- ences an asthma attack. This method could help people reduce their anxi- ety by managing their exposure to air pollution when levels are moderate or high. Figure 5 shows the MobAsthma application’s system architecture and main components. Sound Level 35 dB An initial trial of MobAsthma is Lg=0.0368366666666667 under way in collaboration with Jon Lt=32.2368383333333 Ayres, a professor of environmental (a) (b) and occupational medicine at the Uni- Options Back versity of Aberdeen. Mobile Noise Mapping Eiman Kanjo Dr Low Noise Levels High Cambridge University of Cambridge Implementations So far, we’ve implemented our phone software on N95 and N80 using native Symbian C++; we chose the latter be- cause it lets phone software access all our development APIs. MobSens software components in- stalled on the phones must perform the following operations: sensing, fil- tering, processing, and logging sensor data; rendering screen displays, includ- ing graphs, maps, and user interfaces; and uploading data streams to back- end servers in real time. Each data entry is combined with the last valid GPS location plus additional informa- tion such as latitude, longitude, speed, bearing, UTC date, time, the phone’s (c) International Mobile Equipment Iden- tity (IMEI), user name, journey ID, and Figure 3. NoiseSpy. (a) A user carrying an N95 mobile phone with an external GPS phone battery level. ready to test the NoiseSpy application. (b) Screenshot of the NoiseSpy interface. (c) Both PollutionSpy and NoiseSpy Google Earth visualization of noise data collected by participants from a local cycling use a standard Bluetooth client-server courier company. architecture to receive data from ex- ternal GPS units. We chose Bluetooth GPS because we tested our applications MobAsthma totype that lets asthma specialists and on both N95 and N80—N80 doesn’t As more of us live in urban areas, the allergists investigate the relationships have a built-in GPS unit, and N95’s ability to monitor and assess air qual- between personal exposure to air pol- built-in GPS has a fairly serious flaw. ity becomes increasingly important for lution and the prevalence of asthma Specifically, the receiver is located un- public health authorities. In particular, and respiratory symptoms. Our smart der the number keypad, which means increased air pollution has been linked phone application analyzes, in real it can get a signal only when the phone to increased rates of asthma. time, measurements from medical de- is open. Even then, it takes a long time MobAsthma is a personalized vices such as asthma peak-flow and to get a signal, which isn’t practical for asthma-monitoring application pro- pollution data through wireless sen- our applications. 54 PER VA SI V E computing
  6. 6. Latest pollution A3 (Q22). I have walked measurement Fresh A1 (Q2). Westerly wind Pollution query A1 (Q3). Yes, too many cars Q3. Is it too 1 -Girton/Histon -Is this area highly noisy in here? polluted? Q2. What is the direction A1 (Q22). By bike of the wind? Answer the question abc A2 (Q2). Coming from M11 A2 (Q22). By bus Q22. How did you travel to this location? OK Cancel Pollution query A2 (Q3). Yes, I am near a rd Figure 4. Fresh. Three people use the Fresh system in Cambridge to learn information about local environmental issues and events. Currently, users have the following logging options for controlling how much data they want to store on the Bluetooth personal area phone: Peak ow • interval (logging multiple times per meter Smart phone day), Database server for allergist and asthma specialist • intensive (logging very frequently, such as every second), Pollution sensors Health and • regular (logging scheduled at regular pollution intervals), record • real time (logging over GPRS only, GPS on the phone only, or both GPRS and on the phone), and • upload (logging files to the server once a day). Figure 5. System architecture of MobAsthma application. In Fresh, users’ phone network cell IDs, questions, and answers are also attached to the data stream. Lookup tables of cell IDs include data for from the client side. The client sends user queries, and GET to obtain infor- each cell tower’s latitude and longi- these calls to the server over HTTP us- mation from the server, such as local tude, which are stored in the remote ing POST and GET requests, with the traffic information. database. parameters passed within the POST re- Because most mobile phone net- quest’s data, then uses the reply to up- Lessons Learned works don’t provide mobile phones date the client application’s state. The and Research Challenges with routable IP addresses, all com- system uses POST to send information Preliminary user studies for these vari- munication requests must be initiated to the server, such as sensor data and ous mobile application prototypes have OCTOBER–DECEMBER 2009 PER VA SI V E computing 55
  7. 7. CONTENT SHARING revealed a range of research challenges the complex data-processing tasks to the phone and sensors is subject to dis- that we must address. the back-end servers. connect when the battery level is low on either the phone or the logger. Power Management Scalability GPS signals suffer the same prob- An important challenge when design- The MobSens framework has the po- lems. Although there have been some ing sensing applications on mobile tential to open a large-scale real-time advances in improving GPS receiver phone platforms is power consump- environmental monitoring network sensitivity and new techniques such as tion—in particular, when the appli- around a city. However, such a devel- assisted GPS that permit a GPS receiver cation uses multiple communication opment presents new and formidable to use attenuated signals, a conven- interfaces such as Bluetooth, GPS, concerns arising from the need to tional receiver’s antenna must have a Wi-Fi, and GPRS. We used Nokia’s transmit, integrate, model, and inter- direct line of sight to the GPS satellites. Energy Profiler for power manage- pret vast quantities of highly diverse Our users reported some problems with ment; this standard software tool spatially and temporally varying sensor GPS coverage in Cambridge due to the high urban canyons. We’re looking at ways to enhance Processing sensor data on mobile phones our location-tracking techniques— for example, by working with HW- is computationally intensive and could Communications (www.hwcomms. to use its GSM-based cellular consume a considerable amount of energy, positioning alongside GPS tracking technology. potentially limiting the system’s usability. Privacy specifically lets developers test and data. For example, maintaining sensor Our users’ top concern was the secu- monitor their applications’ energy us- reliability and software consistency rity and confidentiality of the data we age in real time in the target device. across more than 70 MobSens nodes collected about them. Almost everyone We found that Bluetooth communi- raises challenges in terms of dealing questioned the privacy and integrity of cations, screen display, sensor data with failed mobile nodes and network the sensed data itself, especially in cor- processing, and GPRS radios were links, potentially frequent updates relation to the sensing devices. Some responsible for draining most of the from several users, and the impact of potential users refused to take part in battery power. software updates on node reliability. some of our experiments because they As application developers, we want We’re looking at ways to enable up- feared their sensitive personal informa- to build tools that offer good fidel- dating the phone application on us- tion would leak during data collection. ity and user experience without sig- ers’ handsets automatically, perhaps However, many users trusted our sys- nificantly altering a standard mobile by adopting a client-server similar to tem because they knew that we would phone’s operational lifetime. One MUPE (Multi-User Publishing Envi- guarantee their anonymity. way to reduce mobile phone power ronment; in which us- In MobSens, we chose to compro- consumption is to let users switch off ers do a single install only and the cli- mise the device’s usability by designing screen displays and graphs at their ent updates itself once new versions are the interface in a way that let the user own discretion. Similarly, we could available. modify the state of the communica- add a timer to let users change the tion and sensing modalities supported sampling rate from the sensors ac- Handling Disconnections on the phone (audio, Bluetooth, and cording to the task at hand, which Although connectivity is generally GPRS). Nevertheless, the system re- might not require the frequent use of good in urban environments, we found quires a better mechanism to let trusted radios for communication or satel- that sensors still aren’t connected 100 users share sensing presence at high fi- lite signals for location coordinates. percent of the time. This intermittent delity (and present a less accurate view Finally, processing sensor data on connectivity leads to delay-tolerant to less trusted viewers). mobile phones is computationally in- sensing, in which data is cached for a Ongoing work in MobSens has be- tensive and could consume a consid- time before it’s uploaded when the con- gun to address these challenges by pro- erable amount of energy, potentially nection is restored. viding privacy policies that inform us- limiting the system’s usability. With During our experiments, users expe- ers about our data-handling practices this in mind, we could do some basic rienced another form of wireless dis- up front and serve as the basis for a data filtering on the phone and push connection: the connection between user’s decision to release data. We’re 56 PER VA SI V E computing
  8. 8. also hoping to develop efficient veri- the AUTHORS fication protocols that ensure secure Eiman Kanjo is a researcher at the Computer Laboratory and Mathematical data management and guarantee sys- Sciences Centre at the University of Cambridge. Her main research interest is in mobile and pervasive sensing. Kanjo has a PhD from the University of Abertay tem integrity. Dundee in the area of pervasive and tangible interfaces based on computer vi- sion and interactive tabletops. Her PhD work was patented in 2006. Kanjo is a W member of the ACM. Contact her at e’re currently work- ing on revising some of the components and Jean Bacon is a professor of distributed systems at the Computer Laboratory, improving a few archi- University of Cambridge, and a Fellow of Jesus College. She leads the Opera tectural elements to reflect the valuable Research Group, with a focus on the design and deployment of open, large- feedback from our study participants. scale, widely distributed systems. Bacon was the founding editor in chief of IEEE Distributed Systems Online and served as EIC of IEEE Concurrency. She is an Specifically, future revisions of the IEEE and BCS Fellow and has served on the governing body of the IEEE Com- MobSens framework will include puter Society. Contact her at • an improved software module on the phone that prolongs battery life; David Roberts is a research manager at Nokia. With his coding skills receding • an enhanced version of the Web por- faster than his hairline, he recently jumped into technical management posi- tions, the past seven years of which have been at Symbian. During his time tal, ideally one that reduces wait time at Symbian and Nokia, Roberts has headed up a consultancy group working and the amount of data shown to the closely with Nokia on S60 device creation before moving into research. Con- user; tact him at • an improved privacy policy setting as well as an enhanced user interface; • a new way to handle mobile sensors Peter Landshoff is professor emeritus of mathematical physics at the Uni- and more advanced data aggregation versity of Cambridge, where he has been closely involved in the creation, algorithms; and then the running, of several projects, including the Newton Institute, • on-device visualization tools and the Cambridge eScience Centre, the National Institute for Environmental eScience, the Cambridge Computational Biology Institute, the Centre for mapping; and Quantum Computation, and the Millennium Mathematics Project. Contact • software that works with any smart him at phone on an open platform. Visit the MobSens Project (www. for the lat- est in mobile sensing research. 3. A. Campbell et al., “PeopleCentric Urban Sensing,” Proc. 2nd ACM/IEEE Ann. Int’l Wireless Internet Conf. (WICON 06), ACM Press, 2006; http://portal.acm. ACKNOWLEDGMENTS org/citation.cfm?id=1234179. Visit us on the Web We thank our many collaborators on these experi- 4. E. Paulos, R. Honicky, and E. Goodman, ences. We’re also grateful to Mike Short and Ian “Sensing Atmosphere, Workshop on Sens- Curran from O2 UK for providing unlimited GPRS ing on Everyday Mobile Phones,” Proc. airtime and Nokia for providing us with a large Conf. Embedded Networked Sensor number of mobile phones for testing. We’re also Systems (SenSys 07), ACM, 2007; http:// grateful to Symbian for its support. 5. T. Simonite, “Cyclists’ Cellphones Help REFERENCES Monitor Air Pollution,” New Scientist, 2 Jan. 2008; http://technology.newscientist. 1. T. Weber, “How Smart Does Your com/article/dn13130-cyclists-cellphones- Phone Need to Be?”, 8 help-monitor-air-pollution.html. Nov. 2007; business/7078877.stm. 6. E. Kanjo and P. Landshoff, “Fresh: CellID Based Mobile Forum for Commu- 2. J. Burke et al., “Participatory Sensing,” nity Environmental Awareness,” Proc. Proc. World Sensor Web Workshop, ACM Ubiquitous Sustainability: Citizen Sci- Press, 2006; ence & Activism, Ubicomp, 2008; www. wsw2006/6_Burke_wsw06_ucla_final. pdf. shtml. OCTOBER–DECEMBER 2009 PER VA SI V E computing 57