LiPS: Linked Participatory Sensingfor Optimizing Social ResourceAllocation

Mina SakamuraKeio University5322 EndohFujisawa, Kanagawa, Japanmina@ht.sfc.keio.ac.jp

Takuro YonezawaKeio University5322 EndohFujisawa, Kanagawa, Japantakuro@ht.sfc.keio.ac.jp

Jin NakazawaKeio University5322 EndohFujisawa, Kanagawa, Japanjin@ht.sfc.keio.ac.jp

Kazunori TakashioKeio University5322 EndohFujisawa, Kanagawa, Japankaz@ht.sfc.keio.ac.jp

Hideyuki TokudaKeio University5322 EndohFujisawa, Kanagawa, Japanhxt@ht.sfc.keio.ac.jp

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http://dx.doi.org/10.1145/2638728.2641286

AbstractThis paper proposes a concept of linked participatorysensing, called LiPS, that divide a complex sensing taskinto small tasks and link each other to optimize socialresource allocation. Recently participatory sensing havebeen spreading, but its sensing tasks are still very simpleand easy for participants to deal with (e.g. Please inputthe number of people standing in a queue. etc.) . Toadapt to high-level tasks which require specific skills suchas those in engineering, the medical profession orauthority such as the organizer of the event, we need tooptimize social resource allocation because the number ofsuch professionals are limited. To achieve the complexsensing tasks efficiently, LiPS enables to divide a complexsensing task into small tasks and link each other byassigning proper sensors. LiPS can treat physical sensorsand human as hybrid multi-level sensors, and task providercan arrange social resource allocation for the goal of eachdivided sensing task. In this paper, we describe the designand development of the LiPS system. We alsoimplemented an in-lab experiment as the first prototype ofhybrid sensing system and discussed the model of furthersystem through users’ feedback.

Author KeywordsParticipatory Sensing; Mobile Sensing; Sensor Networks;Valuing Information; Integrated Sensing Architecture;XMPP

ACM Classification KeywordsH.4.2 [Information Systems Applications]: Types ofSystems—Decision support,Logistics; J.0 [ComputerApplications]: General; H.5.2 [Information Interfaces AndPresentation (e.g., HCI) ]: User Interfaces—User-centereddesign

General TermsAlgorithms,Design,Experimentation,Human Factors

IntroductionRecent progress of mobile devices such as smartphonesenable human to leverage their perception ability as a partof sensing framework. This sensing framework is so calledparticipatory sensing[6] . In participatory sensing, taskprovider can define a sensing task. Current participatorysensing tasks are very simple and easy for participants toachieve (e.g. Please input the number of people standingin a queue. , Please take a photo of the weather todayetc.) . We consider participatory sensing can extend forhigh-level sensing tasks which can be achieved by peoplewho have specific skills such as those in engineers, themedical professions or authorities such as the organizer ofthe city event. However, since the number of such skilledpeople is limited, it is necessary to allocate them toappropriate tasks which has high-priority for sensing.

We can find the example of optimized human resourceallocation in a hospital; sensing a pulse of a patient by aphysical sensor and when the physical sensor detectsunusual data, the system would call a nurse. Then, the

nurse would come to see and check. If the nurse stillcould not solve the problem by herself, the nurse wouldimmediately call a doctor, who is a professional in themedical field. Then the doctor would attach additionalphysical sensors or call another doctor. In that situation, aphysical sensor works as level 1 sensor to get simplequantitative data, a nurse works as level 2 sensor to getqualitative data, and a doctor who is a medical professionworks as level 3 sensor, giving their task to other sensor.After each sensor fulfills their role and their task need tolevel up, each sensor can give their task to other sensor,because the tasks and the sensors related to the event areall linked. Because the number of such professional islimited, a doctor can not see a specific patient all thetime. Therefore, a doctor would not be called before aphysical sensor or a nurse call for help, except doctors’round visits and so on. Similarly, social resource,especially people who have specific skills or authority, arelimited and do not have enough time to deal with a task.To reduce the labor of people and such professionals anduse their time effectively, we need to optimize socialresource allocation. To achieve that, we divide a sensingtask into small tasks and link each other by assigningproper sensors. We call this concept as linkedparticipatory sensing (LiPS).

Figure1 shows the concept of linked participatory sensing.A task provider can define any sensing task such as safetymanagement in a train station and understanding asituation in a shopping mall (e.g. clearance, congestiondegree, feelings of customers, suspicious object detectionetc.).

Sensing Task

Level 1 Task

Level 2 Task

… Final level Task

Physical sensor

People Professional

Task provider

Physical sensor or People

Figure 1: Concept of linked participatory sensing.

One sensing task can be divided into some mutiple tasks,and a task provider can decide which sensor to use forachieving a goal of each small task. We treat physicalsensors and humans as hybrid multi-level sensors. Forexample, we use a physical sensor to complete level 1 taskin figure1, but we can replace them with other sensorsaccording to circumstances. Divided small tasks are alllinked, and if a previous sensor fulfilled a role and detectsa certain situation which requires further detail sensing,the next sensor would start to work. For example, physicalsensors can get quantitative information, but they can notget qualitative information which requires a humanperception to get(e.g. atmosphere, state, context of thesituation). In this way, by dividing one sensing task intosmall tasks and linking them, we can optimize socialresource allocation, and sense the complex task efficientlyand reasonably.

Our contributions in this position paper are as follows:

• Presenting LiPS, which divides a complex task intosmall tasks, links each of those to optimize socialresource allocation and manages the divided tasks.

• Implementing and evaluating the first prototype ofhybrid sensing system with people and physicalsensor

• Indicating guidelines for design about furtherapplications for participatory sensing integrated withphysical sensors

Sensing Task Examples with LiPS SystemTable1 presents sensing task examples with LiPS. In thisexample, physical sensors are used as level 1 sensor forgetting quantitative data such as illuminance,temperature, noise, acceleration and so on. In addition,people as level 2 sensor are linked to level 1 sensor forgetting qualitative data such as atmosphere, state,context of the situation. The sensing tasks, such asunderstanding a situation in a shopping mall(e.g.clearance, congestion degree, feelings of customers,suspicious object detection etc.), traffic conditions, safetymanagement and public management property are definedby task providers. Task providers should be able toarrange social resource to each small task whenever itlikes. For social resource, we can use not only stationaryphysical sensors, people and professionals but also websensors(e.g. analyzing twitter/foursquare/facebook datato watch a situation), movable physical sensors( e.g.attached on cars and trains) and so on.

About the situation in shopping mall, the professionalwould be sanitary engineer, manager of shopping mall, or

Table 1: Sensing task examples with LiPS system

Sensing task Level 1 task Level 2 task Level 3 task

Situation inshopping mall

Existence of a person,Weight of trash can

Clearance, Congestion degree,Feelings,

Suspicious object detection

Flow of people in shopping mall,Detailed information about clearance

and safety of people

Row of buses,taxies Existence of a personCongestion information,

Waiting time

Flow of bus/taxi services,

Buses/taxies allocation andlocation information

Usages ofa meeting room Door,Illuminance Trends in use

Detailed information about clearance,usages

The context oftrash can

IlluminanceWeight Contents

Clearance,Suspicious object detection

Public propertye.g. a streetlight Illuminance Electricity failure Detailed condition for repairs

security guard. At first, if physical sensors detect existenceof people, the system would ask people about clearance,congestion degree, their feelings or any suspicious object.Then, if something happened in the actual place,professionals would take a real action to solve the problemin real time. For traffic conditions such as rows of busesand taxies, by using physical sensors, we can detect theexistence of people but we can not get detailedinformation such as how a line is moving forward, howmany people are standing in line and so on. Therefore, weask users to get specific congestion information and tellmanager,security guard or bus/taxi drivers. Sensors canbe used for usages of meeting room which we can detectif a door is open or not by an acceleration sensor, and ifthe light is on or not by an illuminance sensor. If somesort of change was found by physical sensors like comingsand goings and beginnings of meetings, then we ask usersto report status of use, how many people is precisely inthe room, and when the meeting would be finished.Usages of rooms can be shared by a group and room

managers. This is not only for meeting room, but also forclass room, institute and any event. Sensors also can beused in the context of trash can, if we put an illuminationsensor or a sensor that can detect weight, users would beasked to report about contents in the trash can whensomeone throws away trash. Collected information can beuseful for a sanitary engineer to comprehend surroundingenvironment. Moreover, those used in public property, forexample an illumination sensor on a streetlight, we candetect whether a streetlight is on or not when it is dark.Users would check if a streetlight was not on for somereason even when it is dark. If an electricity failure wasdetected, an administrative bureau could go check and fixit. Various application examples could be consideredbesides these given examples.

Related workRelated work can be mainly classified into three domains:integrated architecture for sensor networks andparticipatory sensing, research on assigning participatory

sensing tasks to each proper sensor, and tools for tasksetting and facilitation of providing tasks. We review eachof these areas below.

Integrated architecture for sensor networks and participa-tory sensingFor example, Suelo[11] is an assisted sensing system. Itactively requests the help of a human when necessary tovalidate, calibrate, repair, or replace sensors because manysoil sensors are inherently fragile and often produce invalidor uncalibrated data. On the other hand, LiPS valueparticipatory sensing tasks by physical sensors and we tryto optimize sensing environment to reduce the humancost. Moreover in Suelo, they request the help of a humanto complement sensing by physical sensors, but in LiPS weuse physical sensors to complement sensing by people.

Assigning participatory sensing tasksThere are some researches on assigning participatorysensing tasks to suitable people by a proper way. As anexample of a proper way, incentive mechanisms forsmartphone collaboration in data acquisition anddistributed computing[8] uses game theory to consider themost suitable reward for participation. To achieveparticipatory sensing tasks, they predict users’ decisionmaking and suggest a reward price for clients. However,these researches are subjective valuing. By using physicalsensors, we valued participatory sensing tasks in anobjective way and it will be the new guideline for futureapplications.

Tools for task setting and facilitation of providing tasksThere are many researches in tools or systems for tasksetting and facilitation of providing tasks to build anapplication for participatory sensing, such as sensr[9],medusa [10] and prism[7]. However, these are thoroughlyfor participatory sensing system. Our research

implemented the same platform that works forparticipatory sensing and sensor networks. We built asystem that could reduce a user’s load for setting a taskand provide information easily.

In contrast to these existing works, we focused onmultiple tasks while existing researches target only onetask. We assume the situation where many complicatedtasks exists in the future. To optimize sensingenvironment and reduce the human cost, we provide aflexible sensing method by combining physical sensors andvariously skilled human as hybrid multi-level sensors.

System architecture of LiPSWe designed an integrated architecture for sensornetworks and participatory sensing to link each sensor.XMPP[5] is the general term for protocol, client andserver of open-source instant messenger based on XML.The specifications of XMPP is all open to the public andone of the characteristics is that only if you had yourdomain and server, you could start your own XMPPserver. Moreover, XMPP has a strong security and is veryflexible because XEP(XMPP Extension Protocol) isoffered. XEP is the specification of various extensions forsuch as group chat and video chat. In our architecture, weused publish-subscribe extension[4] of XEP. Usingpublish-subscribe extension, we can publish sensor datafrom both physical sensors and participatory sensing to avirtual event node which is defined as a task. Also, wecan subscribe to published information easily. Whenapplication use sensor data from both physical sensors andusers, it is not so important for the application whethersensor data is gathered from physical sensors or users. Forthe application, it would be enough if it could get only tbenecessary information, so it is desirable that we get sensordata by same way of access and API. Therefore, in our

architecture, we used publish-subscribe extension that canget sensor data by the same API.

Our architecture is based on Sensor Andrew[1], which isdeveloped in Carnegie Mellon University. Sensor Andrew isa middleware that can comprehensively handle sensor dataof different sensor nodes and implemented on XMPPframework. When we use sensor data from physicalsensors and users for application, we need to know a dataformat. So in our architecture as same as Sensor Andrew,we treat a virtual event node(task) as a pair, meta nodeand data node. Meta node has an information format ofsensor data of either physical sensors or participatorysensing. For example, sensor id, unit, name and so on. InSensor Andrew, some units about physical sensors such aslatitude, longitude, kelvin have been already defined.However, units about participatory sensing such as textinput, multiple selection, photos, animation and voiceinput have not been defined yet so we extendedSensor-Over-XMPP[2]. By giving a description ofdefinition about participatory sensing in meta node thesame as physical sensors, we can enable gettinginformation about a user interface dynamically andgenerate suitable user interface for each taskautomatically when users participate in sensing. On theother hand, data node has information about sensor datafrom physical sensors, text input and photos that usersreported about tasks. In our architecture, meta node anddata node form a pair as one event node, and we canpublish or subscribe to the event node.

Task Provider

Task di�nition

Sensordata

LiPS

Sensing Task Analyzing Module

Sensing Task Assigning Module

Task Contributor

Sensor data

Sensing task

Sensor data

Sensing Task Manager

VS:pubsub node as Virtual Sensor

VS1 VS2 VS3

XMPP Server

Level 1 task Level 3 task...Level 2 task

Physical sensor People Professional

Task Create

. . . .

Figure 2: System architecture.

Figure 2 represents a system architecture of LiPS. Taskprovider defines a sensing task which includes three microsensing tasks. According to the task definition, LiPSsystem creates pubsub nodes in XMPP server whichreceive sensor data from task contributor. LiPS firstlyassign level 1 sensing task in the environment. In thefigure, the level 1 sensing task is associated to physicalsensor. When LiPS detects that a certain conditionarrises, which is ruled in task definition, in sensing taskanalyzing module, LiPS assigns level 2 task in theenvironment. This process is periodically repeatedaccording to task definition, so finally professionals will beassigned to the sensing task. Task definition and alsosensor data from each task contributor is written in XMLformat as extended Sensor-Over-XMPP format.Screen of publishing a task

Sun SPOT

Acieving a sensingtask about a meeting room(Participatnt)

Meeting room（SSLab)

The number of people

Event contents

The end expected time

Comment

Screen of publishing a task

Sun SPOT

Acieving a sensingtask about a meeting room(Participatnt)

Meeting room（SSLab)

The number of people

Event contents

The end expected time

Comment

Figure 3: Early prototypeimplementation.

Early Prototype Implementation and Pilot StudyAs a early prototype of our system, we implemented asimple system which enables task provider to deploy asensing task in the environment as two divided smalltasks. These divided sensing tasks can be linked andcarried out by both physical sensor and human. As a pilotstudy with implemented system, we focused to detect acontext of meeting room. As a Level 1 sensor, a physicalsensor (a three-axis acceleration sensor on SunSpot[3]),which is attached to the door of the meeting room,detects opening/closing status of the door. If the systemdetects the door moving, it fires Level 2 sensing taskwhich asks people to report the meeting rooms’ situationsuch as the number of people in the room, eventcontents, the events’ expected end time and a commentthrough Android application (see Figure3). We alsocarried out a pilot study of implemented system toconfirm how people achieve their sensing task bycombining the task based on linked sensing task by aphysical sensor. For the pilot study, sixteen individuals

belonging to our laboratory were recruited and the studyran for a three day span. If a task was needed to update,users were notified by vibration to participate in sensing.The context reported by all users was also available tobrowse on Twitter, to understand present usage of themeeting room through popular service.

As a result, the number of contributions for sensing tasksin three days was 48 in total. Of those, we have got 18posts for the first day, 17 posts for the second day and 13posts for the third day. The minimum was 0 times, themaximum was 12 times and 8 people posted 1 time or 2times. After the door opening/closing was detected atleast once, it sometimes took 2 or 3 hours to update, butmost of the contributions were updated within 15minutes. 16 events during 11am to 23pm were detectedby users’ post and there was 1 event that could not bedetected. We did not consider the hours in the middle ofthe night after the meeting room use was finished on thatday. As a study, the number of the contributions differsamong respective users considering that the minimum is 0and the maximum is 12, but the detected events coveredalmost all of the events. In the future we will get moreusers’ cooperation and try to reduce the achievement timeof the task. Furthermore, the needed time forcontributions depended on each task so when the numberof tasks are increased, it would be necessary to sort thetasks based on the urgency of update.

We also carried out a questionnaire survey to participants.About attaching physical sensor to the door as level 1sensor, all people answered that it was suitable to see thestatus if the door of the meeting room is opening/closing.However, about the timing of requesting people to report,many people answered neutral and the reason was “Toomany changes.” As a study, it sometimes impose a load

on a user because of too much detection including pickingup a baggage, food and drink. It was not enough to see ifthe door was opening/closing, so we have to think aboutadopting various physical sensors. In the future we willuse various physical sensors to value to satisfy the userneeds. For example, using an illumination sensor to detectthe ON/OFF of the light. About motivation of a user toparticipate, more than half of the users answered thattheir motivation raised. The reason was as follows: “Iwant to adopt the application if a contribution to tasks isnot a big burden on me,” “It was good to see my postsand the other posts on Twitter,” “It was easy for everyoneto participate,” “My motivation raised becauseinformation that I wanted to get could be acquired by thissystem.” Also more than half of the users answered thattheir opportunity to participate in sensing was increasedby our system. The reason were “I had not usually caredabout participating before the experimentation,” “Byvaluing and visualizing I am sure that there were moreopportunities to participate than voluntary contribution.”As a result, we found that linked small tasks can optimizesensing environment by using a physical sensor whenpeople did not have to get involved in sensing. Thoughwe have to think about which sensor to allocate for eachproper task, we could understand the situation of themeeting room, by combining the task based on linkedsensing task by physical sensor and human resource. Inthe future, we will adopt more physical sensors and see aresult from long-range experiment.

Conclusion and future workRecently participatory sensing have been spread, but itssensing tasks are still very simple and easy for participantsto deal with. To adopt high-level tasks which requirespecific skills such as in engineering, the medicalprofession or authority such as the organizer of the event,

we need to optimize social resource allocation because thenumber of such professionals are limited. To achieve thecomplex sensing tasks efficiently, we proposed a conceptof linked participatory sensing, called LiPS, that divide acomplex sensing task into small tasks and link each otherto optimize social resource allocation. Using ourarchitecture, we can make various sensing environment aswe showed in the sensing task examples, being able to useboth inside and outside. In this work, we implementedand evaluated the first prototype of hybrid sensing systemwith people and physical sensor. As a result, we foundthat linked small tasks can optimize sensing environmentby using a physical sensor when people did not have toget involved in sensing. In this way, by allocating socialresource to each proper sensing task, task provider cansense more complex environment that we could not do soin participatory sensing in the past. As a preliminaryexamination, it was good to get the users’ feedback, butthe sensing task of the meeting room was too simple.Therefore, we have to extend our experiment using morecomplex sensing tasks which can prove LiPS architectureand see how each sensor can work and link each other fora long span. In addition to sensing task examples withLiPS system which we described in this paper, we willthink about sensing tasks that not only task providers butalso physical sensors and general people can divide andassign thier small sensing tasks.

AcknowledgementsThis research was partly supported by National Instituteof Information and Communications Technology (NICT).

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