Mobile and Ubiquitous Systems Computing. Networking, and Services

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123

Ivan Stojmenovic

Zixue Cheng

Song Guo

(Eds.)

Mobile and Ubiquitous

Systems: Computing,

Networking, and Services

10th International Conference, MOBIQUITOUS 2013

Tokyo, Japan, December 2–4, 2013

Revised Selected Papers

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Editorial Board

Ozgur Akan

Middle East Technical University, Ankara, Turkey Paolo Bellavista

University of Bologna, Bologna, Italy Jiannong Cao

Hong Kong Polytechnic University, Hong Kong, Hong Kong Falko Dressler

University of Erlangen, Erlangen, Germany Domenico Ferrari

UniversitàCattolica Piacenza, Piacenza, Italy Mario Gerla

UCLA, Los Angels, USA Hisashi Kobayashi

Princeton University, Princeton, USA Sergio Palazzo

University of Catania, Catania, Italy Sartaj Sahni

University of Florida, Florida, USA Xuemin (Sherman) Shen

University of Waterloo, Waterloo, Canada Mircea Stan

University of Virginia, Charlottesville, USA Jia Xiaohua

City University of Hong Kong, Kowloon, Hong Kong Albert Zomaya

University of Sydney, Sydney, Australia Geoffrey Coulson

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Mobile and Ubiquitous

Systems: Computing,

Networking, and Services

10th International Conference,

MOBIQUITOUS 2013

Tokyo, Japan, December 2

_–

4, 2013

Revised Selected Papers

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Ivan Stojmenovic University of Ottawa Ottawa, ON Canada

Zixue Cheng Song Guo

School of Computer Science and Engineering

The University of Aizu Tsuruga Fukushima

Japan

ISSN 1867-8211 ISSN 1867-822X (electronic) ISBN 978-3-319-11568-9 ISBN 978-3-319-11569-6 (eBook) DOI 10.1007/978-3-319-11569-6

Library of Congress Control Number: 2014949557 Springer Cham Heidelberg New York Dordrecht London

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Printed on acid-free paper

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MobiQuitous 2013 has provided a successful forum for practitioners and researchers from diverse backgrounds to interact and exchange experiences about the design and implementation of mobile and ubiquitous systems.

We received 141 technical papers from all around the world. All submissions received high-quality reviews from Technical Program Committee (TPC) members or selected external reviewers. According to the review results, we have accepted 52 regular papers and 13 short papers for inclusion in the technical program of the main conference.

In the main technical program, we had two inspiring keynote speeches by Prof. Xuemin (Sherman) Shen from University of Waterloo, Canada and Prof. Nei Kato from Tohoku University, Japan, and 12 technical sessions, including 10 regular-paper ses-sions and two short-paper sesses-sions. Besides the main conference, we also had a joint International Workshop on Emerging Wireless Technologies for Future Mobile Net-works (WEWFMN 2013). The conference successfully inspired many innovative directions in thefields of mobile applications, social networks, networking, and data management and services, all with a special focus on mobile and ubiquitous computing. It is our distinct honor to present the best paper, Focus and Shoot: Efficient Iden-tification over RFID Tags in the Specified Area, and the best-student paper, Protecting Movement Trajectories Through Fragmentation, for MobiQuitous 2013. The two papers were voted out based on the reviewers’ _{recommendations and on the papers}’ significance, originality, and potential impact.

The technical program is the result of the hard work of many individuals. We would like to thank all the authors for submitting their outstanding work to MobiQuitous 2013. We offer our sincere gratitude to the technical committee members and external reviewers, who worked hard to provide thorough, insightful, and constructive reviews in a timely manner. We are grateful to the Steering Committee and Organizing Committee of MobiQuitous 2013, and especially to the TPC Chairs, Prof. Guojun Wang from Central South University, China, Prof. Kun Yang from University of Essex, UK, Prof. Amiya Nayak from University of Ottawa, Canada, Prof. Francesco De Pellegrini from Create-Net, Italy, and Prof. Takahiro Hara from Osaka University, Japan for their invaluable support and insightful guidance. Finally, we are grateful to all the participants in MobiQuitous 2013.

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Organization

Steering Committee

Imrich Chlamtac Create-Net, Italy

Fausto Giunchiglia University of Trento, Italy

Tao Gu University of Southern Denmark, Denmark

Tom La Porta Pennsylvania State University, USA Francesco De Pellegrini Create-Net, Italy

Chiara Petrioli Universita di Roma“_{La Sapienza}”_{, Italy} Krishna Sivalingam University of Maryland at Baltimore, USA Thanos Vasilakos University of Western Macedonia, Greece

Organizing Committee

General Chairs

Zixue Cheng University of Aizu, Japan Ivan Stojmenovic University of Ottawa, Canada

General Co-chair

Song Guo University of Aizu, Japan

TPC Chairs

Guojun Wang Central South University, China

Kun Yang University of Essex, UK

Amiya Nayak University of Ottawa, Canada Francesco De Pellegrini Create-Net, Italy

Takahiro Hara Osaka University, Japan

Local Chair

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Baoliu Ye Nanjing University, China

Shanzhi Chen Datang Telecom Technology & Industry Group, China

Publicity Chair

Shui Yu Deakin University, Australia

Susumu Ishihara Shizuoka University, Japan Hirozumi Yamaguchi Osaka University, Japan

Publication Chair

Lei Shu Guangdong University of Petrochemical Technology, China

Web Chair

Deze Zeng University of Aizu, Japan

Conference Manager

Ruzanna Najaryan EAI, Italy

Technical Program Committee

Jemal Abawajy Deakin University, Australia

Muhammad Bashir Abdullahi Federal University of Technology, Minna, Nigeria Christian Becker University of Mannheim, Germany

Roy Campbell University of Illinois at Urbana-Champaign, USA Jiannong Cao Hong Kong Polytechnic University, Hong Kong Iacopo Carreras Create-Net, Italy

Liming Chen University of Ulster, UK

Marcus Handte University of Duisburg-Essen, Germany

Min Chen Huazhong University of Science and Technology, China

Franco Chiaraluce Polytechnical University of Marche, Italy

Michel Diaz LAAS-CNRS, France

Pasquale Donadio Alcatel-Lucent, Italy

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Kary Framling Aalto University, Finland Chris Gniady University of Arizona, USA

Teoﬁlo Gonzalez University of California at Santa Barbara, USA Sergei Gorlatch University of Münster, Germany

Yu Gu Singapore University of Technology and Design, Singapore

Deke Guo National University of Defense Technology, China Clemens Holzmann University of Applied Sciences Upper Austria,

Austria

Henry Holtzman MIT Media Lab, USA

Susumu Ishihara Shizuoka University, Japan Yoshiharu Ishikawa Nagoya University, Japan

Xiaolong Jin Institute of Computing Technology, Chinese Academy of Sciences, China

Jussi Kangasharju University of Helsinki, Finland

Stephan Karpischek Swisscom (Switzerland) AG, Switzerland

Fahim Kawsar Bell Labs, USA

Yutaka Kidawara NICT, Japan

Matthias Kranz Universität Passau, Germany

Mo Li Nanyang Technological University, Singapore

Xu Li Huawei Technologies, Canada

Zhenjiang Li Nanyang Technological University, Singapore Xiaodong Lin University of Ontario Institute of Technology,

Canada

Hai Liu HongKong Baptist University, Hong Kong

Yunhuai Liu TRIMPS, China

Tomas Sanchez Lopez EADS Innovation Works, UK Rongxing Lu University of Waterloo, Canada Xiaofeng Lu Xidian University, China

Oscar Mayora Create-Net, Italy

Iqbal Mohomed IBM T.J. Watson Research Center, USA Felix Musau Kenyatta University, Kenya

Mirco Musolesi University of Birmingham, UK Sushmita Ruj Indian Institute of Technology, India Hedda R. Schmidtke Carnegie Mellon University, USA

Joan Serrat Universitat Politècnica de Catalunya, Spain Zhenning Shi Orange Labs Beijing, China

Hiroshi Shigeno Keio University, Japan

Stephan Sigg National Institute of Informatics, Japan

Philipp Sommer CSIRO, Australia

Danny Soroker IBM T.J. Watson Research Center, USA Mineo Takai UCLA, USA and Osaka University, Japan

Ning Wang University of Surrey, UK

Song Wu Huazhong University of Science and Technology, China

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Jianming Zhang Changsha University of Science & Technology, China

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Abstract. Opportunistic sensing becomes a competitive sensing para-digm nowadays. Instead of pre-deploying application-specific sensors, it makes use of sensors that just happen to be available to accomplish its sensing goal. In the opportunistic sensing paradigm, the sensors that can be utilized by a given application in a given time are unpredictable. This brings the Semantic-Web based situation inference approach, which is widely adopted in situation-aware applications, a major challenge, i.e., how to handle uncertainty of the availability and confidence of the sensing data. Although extending standard semantic-web languages may enable the situation inference to be compatible with the uncertainty, it also brings extra complexity to the languages and makes them hard to be learned. Unlike the existing works, this paper developed a situation infer-ence tool, namedOPSitu, which enables the situation inference rules to be written in the well accepted standard languages such as OWL and SWRL even under opportunistic sensing paradigm. An experiment is also described to demonstrate the validity ofOPSitu.

Keywords: Semantic web

·

Situation inference

·

Opportunistic sensing

1 Introdution

In the research of situation-aware systems, situation inference is considered to be an important technique, which focuses on how to infer the situation of an entity (i.e. a person, a thing or a place) based on sensing data collected from the physical space or the cyberspace [1]. Among multiple approaches for sit-uation inference, the Semantic-Web based approach is widely adopted [2–6]. In this approach, standard Semantic Web languages, such as OWL (Web Ontol-ogy Language) and SWRL (Semantic Web Rule Language), are used to model the related concepts and inference rules at design time. After obtaining the sens-ing data, situation inference process is conducted by a semantic inference engine

c

Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2014 I. Stojmenovic et al. (Eds.): MOBIQUITOUS 2013, LNICST 131, pp. 3–16, 2014.

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at runtime. In these works, there is a common assumption that the sensing data are certain and complete during the inference process [1].

In recent years, with the technological advance and popularity of IOT (Inter-net of Things) and mobile computing, sensing infrastructures have been estab-lished in our daily surroundings. The massively existing sensing devices include static sensors spreading across buildings, streets, public parks and rivers, and mobilizable sensors carried by people and vehicles, such as built-in sensors in smartphones, tablets, wearable devices, vehicles borne radars, GPS, cameras, etc. Together with the sensors, wireless communication infrastructures, such as WSN, Wi-Fi and 3G/4G mobile network, are also available almost everywhere to deliver sensing data. With these abundant sensors and sensing data deliv-ery infrastructures, a new sensing paradigm emerges, which is referred to as Opportunistic Sensing [7–11]. Instead of pre-deploying application-specific sen-sors, opportunistic sensing applications make use of sensors that just happen to be available to accomplish its sensing goal [11].

Due to the sensor sharing mechanism, the opportunistic sensing paradigm is less costly and more environmental friendly. However, it leads to new technical challenges to those applications that adopt the Semantic-Web based situation inference approach. Firstly, opportunistic sensing attempts to discover and utilize sensors available by chance. Therefore, when there are no sensors to acquire sensing data that is necessary during situation inference process, the situation of an entity cannot be deduced. Secondly, even if all needed sensors are available, the confidence of sensing data is unpredictable. There are two reasons for the unpredictability. On the one hand, the sensors to fulfill a sensing goal are by products of other sensing systems rather than application-dedicated, and the accuracy of the same type of sensors vary dramatically from one sensing system to another. On the other hand, it is hard to predict what sensor will be selected to accomplish a sensing goal at runtime.

The above stated problems may be abstracted as how to do semantic reason-ing with uncertainty. To solve this problem, various extensions of OWL and SWRL have been proposed with different mathematical theories [12]. These works have proved their validity to varying degrees, but they also have a com-mon deficiency, i.e., the extended languages are often very complicated and hard to be learned, even for those people who are familiar with standard Semantic Web languages.

Therefore, this paper developed a situation inference tool, named OPSitu. Instead of extending languages, OPSitu provides the developers of situation-aware applications with standard OWL and SWRL to write the situation inference rules, no matter the application will run under opportunistic sens-ing paradigm or not. The uncertainty of senssens-ing data in opportunistic senssens-ing is handled at runtime by the situation inference engine ofOPSituwith the help of a pre-built knowledge base.

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2 Running Example

A situation-aware application, namedMyClassroom, is to provide different ser-vices for students in classrooms according to their different situations. Thus MyClassroom has to identify current situation of a student in the classroom out of a set of possible situations. There are only five possible situations for a student user in the classroom that MyClassroom focuses, and they are class attendance, open lecture, student meeting, class exam and self-study. To infer the users situation, there are also five contexts to be exploited and the relevant sensing modules to acquire these contexts are described in Table1. MyClass-room is running under opportunistic sensing paradigm, because there are two contexts whose availability is uncertain, i.e., status of the projector and existence of human voice.

Moreover, to infer the student’s situation in classrooms, five rules are given in Table2, and one row for each possible situation. For example, if a student is in a large classroom, the projector in that room is on, human voice exists in that room, and the acquaintance proportion of Tom in that room is low, then Tom

Table 1.Context and Relevant Sensing Modules

Context Relevant Sensing Module Availability Classroom Capacity Observed by human and

stored in the database

Available Projector Status Based on the light sensor on

the screen of projector

Uncertain People Speaks Based on the microphone on

the rostrum

Uncertain Location Based on Wi-Fi fingerprint Available Acquaintance proportion Based on the Bluetooth in

persons smartphone Class Attendance In Classroom Small/Mid/Large On Yes High

Open Lecture In Classroom Large On Yes Low

Student Meeting In Classroom Small/Mid Off Yes High

Class Exam In Classroom Mid/Large Off No High

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is attending an open lecture. By adopting the Semantic-Web based approach, these inference rules are written in OWL and SWRL. In Fig.1, the inference rule for specifying “Open Lecture” is written in SWRL in (a), and related concepts appearing in the rule are defined in the ontology model in (b).

3 System Overview

3.1 Key Concepts

For the convenience of description, some concepts are interpreted in the follow.

Situation & Context. In this paper, a situation is the semantic abstraction about the status of an entity and the adaptions of the situation-aware application are triggered with the change of situations. A context is the information for characterizing the situation of an entity, and a situation is specified by multiple contexts based on human knowledge. For the example in Sect.2, the situation of a student in a classroom is specified by five contexts based on the inference rules in Table2.

Situation Candidate Set (SCS). Generally speaking, although the possible situations of some entities (for example, a person) are infinite, the situations that an application focuses are limited. Therefore, situation inference can be considered as a classification problem. The candidate situations of an entity form a set, which is referred to as an SCS (Situation Candidate Set) in this paper. For the example in Sect.2, the SCS forMyClassroom is S ={Attending Class, Attending Open Lecture, Having Meeting, Taking Exam, Self-Studying}.

Context Assertion (CA). In this paper, Context Assertion (CA) is defined as a logic expression describing the condition that a context should be satisfied. For the example in Fig.1there are five contexts. Correspondingly, there are five Context Assertions (CAs) denoted asA(Ci)(i= 1,2, . . . ,5), and they are listed in Fig.2.

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Fig. 1.Situation Inference Rule: An Example

Fig. 2.Example of context assertions

3.2 Architecture

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Fig. 3.System architecture ofOPSitu

Opportunistic Sensing Data Collector. It consists of sensing modules for different contexts, including location, temperature, light, sound, etc. Those mod-ules obtain sensing data from the physical space or the cyberspace and process them into meaningful context information. This part has been done by many existing works [7–9,11], thus we will not discuss it in detail.

Knowledge Base. Situation-aware applications perform the situation inference based on two types of knowledge. One is shared by all applications, and the other is application-specific.OPSitu is designed according to this classification.

The knowledge shared by applications is stored and managed in a pre-built Knowledge Base. It consists of theShared Ontology and theContext Confidence Record. The Shared Ontology defines commonly used concepts for all applica-tions asClass andProperty in OWL (Web Ontology Language). To address the unpredictability of sensing data’s confidence pointed out in Sect.1 theContext Confidence Record pre-stores the confidence of contexts, which is measured by the accuracy of the sensing data collector. At runtime, the situation inference engine can query theContext Confidence Recordand utilize them in the inference process.

Application-specific knowledge is injected into theKnowledge Baseby appli-cation developers. It is comprised of the App-specific Ontology and the SIRs. The App-specific Ontology is derived from the Shared Ontology. Therefore, it not only contains all concepts in the Shared Ontology but includes some addi-tional concepts just for a specific application.SIRsare logic expressions defining the relationships between contexts and situations, and they are also application-specific.

Since the management of knowledge base is a mature technology and there are many existing tools [13], OPSitu directly adopts Prot´eg´e [14], a free open-source Java tool, to support the creation and management of knowledge in OWL and SWRL.

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main contribution of this paper, and we will describe it in detail in Sect.4.

4 Situation Inference Engine Implementation

For a semantic reasoner that only supports the certain reasoning, two conditions must be satisfied in order to infer the situation of an entity. Firstly, an inference rule is considered as a whole. Secondly, before the inference process is activated, some variables in the rule must be assigned with specific value. However, this is not compatible with opportunistic sensing, because the value of some variable may not be determined when corresponding sensors are not available. To address this problem, theSituation Inference Engine adopts an inference process includ-ing the followinclud-ing three steps. Firstly, it decomposes the SIR into several CAs at first. Secondly, it performs the reasoning for the CAs whose context can be determined at runtime. Thirdly, it merges the reasoning results of all CAs and makes a decision about which candidate situation is the most possible.

4.1 Semi-automatic SIR Decomposition

Although it is easy for human to recognize what is aCAin anSIR, it is difficult to makeOPSitusmart enough to decompose anSIRintoCAs in a fully-automatic way. Thus, we come up with a semi-automatic strategy, and it consists of fol-lowing two steps.

Step 1: Sensible Atomic Formula Selection. After finish writing an SIR at design time, the developer is required by the system to select the atomic formula that are directly related to sensing data (either from physical sensor or cyberspace), which are referred to asSensible Atomic Formulain this paper. For theSIRin Fig.1(a), five atomic formulas,LocatedIn(?p,?r),RoomCapacity(?r, ?cap), HasStatus(?pro,?s), ExistHuman V oice(?r,?x), and Acquaintance-P roportion(?p,?y) should be selected by the developer as Sensible Atomic Formula in this step.

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4.2 Topological-Ordering Based CA Reasoning

After the decomposition,OPSitudirectly exploits Pellet to conduct the reasoning for eachCAwhose context can be acquired. However, the reasoning of eachCAis not independent, and this givesOPSituan opportunity to improve its reasoning performance. Let us take A(C1), A(C3) and A(C4) in Fig.2 as an example to illustrate the dependency issue.

Before runtime reasoning, some variables in aCAhave to be assigned with a specific value. For instance, the value of variable ?r must be assigned before the reasoning of A(C3) and A(C4). This is because only when the room is specified, whether human voice exists and the projector’s status in that room can be determined. Moreover, if one wants to determine where Tom is located in, a query must be issued by using the OWL API [15] getObjectPropertyVal-ues(Tom, LocatedIn). Therefore, the reasoning ofA(C3) andA(C4) depends on LocatedIn(?p,?r). Here we define the dependency betweenCAs in Definition1. According to this definition,A(C3) andA(C4) depends onA(C1).

Definition 1. If the reasoning of A(Ci) depends on the Sensible Atomic For-mula of A(Cj), thenA(Ci) depends onA(Cj).

In fact, after the reasoning ofA(C1), the value of ?r (a specific room) has already been determined. Consequently, if the following two conditions are sat-isfied, theOPSitudoes not need to query the value of ?rwhen reasoningA(C3) andA(C4), thus improving its reasoning performance.

Condition 1: OPSitu performs the reasoning of A(C1) before A(C3) and A(C4).

Condition 2: OPSitu records the value of ?ras an intermediate result after the reasoning ofA(C1).

Based on the analysis above, we propose a method for arranging a reasonable reasoning order so as to enhance the reasoning performance. It consists of two steps, the dependency analysis and the Topological-Ordering based reasoning.

Step 1: Dependency Analysis. In this step, OPSitu will analyze the depen-dency among all CAs of an SIR. In this process, the dependency analysis is designed as the generation of a directed graph, in which aCAis a vertex, and the dependency between twoCAs is a directed edge linking two vertexes.

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function, and to make a decision about which situation is the most possible one. To describe the merging and decision phase, some concepts should be defined at first.

Firstly, the truth-value of a CA is extended from the conventional 0/1(f alse/ true) to the interval [−1,1]. The absolute value of the truth-value indicates the confidence of the context, and the positive/negative symbol represents the assertions tendency of being true or false. The semantic interpretation of this extension is represented in Formula 1, in which k ∈ (0,1] is the confidence of contextCiobtained from theContext Confidence Record in the knowledge base.

T ruthV alue((A(Ci)) =   

k ifacquired Ci indicates that(A(Ci)) is true 0 if theCi can not be acquired

−k ifacquired Ci indicates that(A(Ci)) is false (1) Secondly, CTV (Contexts Truth Vector) and BV (Benchmark Vector) are defined in Definition. In this paper, we assume that all situations in anSCS are based on a common set ofCAs.

Definition 2. An SCS is denoted as S = S1, S2, . . . , Sn the objective of sit-uation inference is to find the sitsit-uation that most likely to be from S. At a given time t, the antecedent of an SIR for Si ∈ S is denoted as R(Si) = A(C1)∧A(C2)∧ · · · ∧A(Cm), where A(Ci) is a CA and the R(Si) is related tom contexts.CTV (Contexts Truth Vector) and BV (Benchmark Vector)are defined in (a) and (b)

(a) DenoteT Vt(Si) = (T1, T2, . . . , Tm) as CTV (Contexts Truth Vector) of R(Si), whereTi =T ruethV alue(A(Ci)), andm is the number of contexts.

(b) DenotebV = (1,1, . . . ,1), am-dimension vector, as theBV (Benchmark Vector).

Thirdly, we define a similarity f unctionSim(S(t), Si) to represent the sim-ilarity between Si and S(t) in Formula 2, where Si ∈ S and S(t)is the actual situation at time t. It is measured by the cosine similarity between Contexts Truth Vector T VtandBenchmark Vector bV.

Sim(S(t), Si) = cos(T Vt(Si), bV) =

T Vt(Si)·bV |T Vt(Si)||bV|

(2)

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Fig. 4.The merging and decision phase description

5 Experimental Evaluation

5.1 Experimental Methodology

To evaluate the validity of OPSitu, an experiment has been conducted based on the example in Sect.2. Firstly, we construct and storeSIRs in Table2 into SWRL format in theKnowledge Base. Secondly, the sensing modules in Table1 serve as theOpportunistic Sensing Data Collectors.

After establishing theSIRsand sensing data collectors, our experiment com-prises two steps:

Step 1: Context Confidence Generation. We generate the confidence of contexts in two ways. One way is through experiment. For example, the confi-dence of projector status is generated by experiment. As the length of the paper is limited, we put the details of four experiments on the website [16]. The other way is set by experience. For example, as the classroom capacity is observed by human and stored in a database, its confidence is set to be a constant 100 % with-out experiment. The generated confidence of each context is listed in Table3, and we store them in the Context Confidence Record of OPSitu’s Knowledge Base.

Table 3.Context Confidence

Context Confidence Generation Method

Classroom Capacity 100 % Experience Projector Status 100 % Experiment

People Speaks 82.5 % Experiment

Location 93.2 % Experiment

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is described in Fig.5. In Step A and B, we simulate an opportunistic sensing environment by programming, and then adopt the Situation Inference Engine ofOPSituto infer the situation in Step C. For the simulation of the opportunis-tic sensing environment, there are two points need to be explained.

(1) Based on a survey in university P, in the step B (2) 90 % of all virtual classrooms are randomly selected to be equipped with light sensors, and 95 % to be equipped with microphone.

(2) In the Step B (3) we assign the value of contexts based on the corre-spondingSIR. This is because the objective of this experiment is to evaluate the validity of Situation Inference Engine of OPSitu rather than the reliability of SIR, thus we assume that allSIRs are reliable in this simulative inference.

Fig. 5.Situation inference in a simulative opportunistic sensing environment

5.2 Experimental Result

We set the number of virtual classroomN to be 10000. The experimental result is demonstrated as the confusion matrix in Table4. By analyzing the confusion matrix in Table4, the overall situation inference accuracy byOPSitureaches to 94.9 % in such a simulative opportunistic sensing environment.

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Table 4.Situation Inference Confusion Matrix

Class Attendance 1830 0 162 0 0

Open Lecture 0 1990 0 0 10

Student Meeting 155 0 1762 83 0

Class Exam 0 0 89 1911 0

Self-Study 0 9 0 0 1991

6 Related Work

To deal with uncertainty in the Semantic Web and its applications, many researchers have proposed extension of Semantic Web language with special mathematical theories. [17,18] extended OWL based on the probability theory. Reference [19] proposed an extension for terminological logics with the possibility theory. Reference [20–23] extended either OWL or SWRL based on fuzzy logic, etc. In addition to language extension, some of these work developed correspond-ing semantic inference engines. In terms of the expressiveness, those extensions for semantic web languages are capable of dealing with the uncertainty brought by opportunistic sensing. However, these extended languages are often compli-cated and hard to learn, even for those who are familiar with the standard semantic web languages. Besides, there are already many situation inference rules written in OWL and SWRL on the Semantic Web. If we want to share and reuse this existing knowledge in opportunistic sensing applications, they have to be transformed into the format of those extended languages. However, since the extended languages are quite complex, the transformation process is very time-consuming.

To avoid the shortcomings of the language extension approaches above, [24] provided a guidance to use OWL and SWRL to express fuzzy semantic rules. This approach models the binary predicate with uncertainty as a 3-ary predicate. Since SWRL is a rule language only supporting unary and binary predicates, this paper adopts a procedure called the reification to express a 3-ary relation via unary and binary relations. Therefore, under this guidance, developers can express fuzzy rules without the modification of OWL and SWRL. However, the reification process is very complicated and it is conducted by developers. Besides, the rules after the reification process, although expressed by SWRL, are too complex to be understood.

7 Conclusion

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of contexts into consideration. In some cases, different contexts contribute to the inference of a situation to different degrees. However, the current version of OPSitu does not consider this aspect. Hence we plan to take the weight of context into consideration in the next version of OPSitu. Secondly, this paper assumes that all candidate situations in an SCS are based on the same set of contexts. However, in some circumstances, this may not be true. Thus we plan to revise the inference engine ofOPSituto make it able to handle more complex conditions.

Acknowledgments. This work is funded by the National High Technology Research and Development Program of China (863) under Grant No. 2013AA01A605, the National Basic Research Program of China (973) under Grant No. 2011CB302604 and the National Natural Science Foundation of China under Grant No.61121063.

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11. Kurz, M., Hölzl, G., Ferscha, A., Calatroni, A., Roggen, D., Tröster, D., Sagha, H., Chavarriaga, R., Millán, J.D.R., Bannach, D., et al.: The opportunity framework and data processing ecosystem for opportunistic activity and context recognition. Int. J. Sens. Wireless Commun. Control, Special Issue on Autonomic and Oppor-tunistic Communications1, 102–125 (2011)

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Eriksson, H., Noy, N.F., Tu, S.W.: The evolution of prot´eg´e: an environment for knowledge-based systems development. Int. J. Hum Comput Stud.58(1), 89–123 (2003)

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Institute of Parallel and Distributed Systems, University of Stuttgart, Stuttgart, Germany

{damian.philipp,jaroslaw.stachowiak, frank.duerr,kurt.rothermel}@ipvs.uni-stuttgart.de

Abstract. Public Sensing (PS) is a recent trend for building large-scale sensor data acquisition systems using commodity smartphones. Limiting the energy drain on participating devices is a major challenge for PS, as otherwise people will stop sharing their resources with the PS system. Existing solutions for limiting the energy drain through model-driven optimizations are limited to dense networks where there is a high prob-ability for every point of interest to be covered by a smartphone. In this work, we present an adaptive model-driven PS system that deals with bothdense and sparse networks. Our evaluations show that this approach improves data quality by up to 41 percentage points while enabling the system to run with a greatly reduced number of participating smart-phones. Furthermore, we can save up to 81 % of energy for communica-tion and sensing while providing data matching an error bound of 1◦_C up to 96 % of the time.

1 Introduction

Public Sensing (PS) is a recent trend for building flexible and large-scale sensor data acquisition systems, facilitated by the proliferation of commodity smart-phones [3]. Modern smartphones feature various sensors such as camera, light intensity, and positioning sensors like GPS. In addition, they offer capabilities for processing and communicating sensor data. Thus, sensor data can be obtained without having to support a fixed sensor network.

In building such PS systems, we face several challenges. On the device side, the main issue is a limited energy supply. While smartphone batteries are fre-quently recharged, keeping the energy consumption for PS minimal, i.e., ensuring that the battery still makes it through a whole day, is a key requirement as other-wise participants may be unwilling to support PS. On the data side, problems are to specify tasks and to deliver data with suﬃcient quality. Due to node mobility, it is likely that each time data is requested, a diﬀerent device is best suited to take readings for the task at hand. However, if we want to minimize the energy consumption on participating devices, querying all smartphones for readings or even proactively collecting location information for all devices is prohibitive.

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These challenges require careful planning which smartphones should take read-ings where and when to ensure that useful data of suﬃcient quality is delivered to a client of the system while keeping the energy consumption minimal.

To address the problem of how to specify interesting data and thus enable flexible PS systems, the concept ofvirtual sensors(v-sensors for short) was intro-duced [12]. V-sensors provide a mobility-transparent abstraction of the PS sys-tem. They are configured to report a set of readings at a client-defined sampling rate at a given position, thus presenting a view on a static sensor network. The PS system then selects nearby smartphones to provide readings for a v-sensor.

However, due to node mobility, some v-sensors may not have any smartphone nearby and may thus be unable to report data readings. Model-driven approaches can be used to ﬁll these gaps with a value inferred from available data [10] and to improve the energy consumption by leaving out v-sensors where values can be inferred with suﬃcient accuracy [13], thus making large-scale PS viable.

However, these approaches are tailored towards dense networks where most v-sensors are well covered (and thus available), e.g., in a busy city center or a business area at lunchtime. For model-driven approaches to provide accu-rate inferred readings, a minimum set of input data from available v-sensors is required. Collecting this minimum set of data is a problem in (partially or com-pletely) sparsely populated areas, e.g., business areas during oﬀ-hours or housing areas during business hours, where the density of smartphones is overall low, or when the most interesting v-sensors are unavailable while many less interesting v-sensors are available.

We address this challenge by presenting an approach for optimized model-driven PS that works in both dense and sparse networks. To this end, we extend our previous model-driven approach. The basic idea is to derive knowledge on v-sensor availability from ongoing query executions. This knowledge is then used in a multi-round approach to iteratively reﬁne the set of v-sensors to query.

In detail, the main contributions of this paper are: (1) An approach for building knowledge on v-sensor availability without extra energy cost. (2) An adaptive query execution model that exploits this knowledge to compensate for unavailable v-sensors, thus making optimized PS viable in both dense and sparse networks. (3) Evaluations analyzing the performance of our approach and showing signiﬁcant improvements compared to previous approaches.

The quality of data obtained by our system is improved by up to 41 percent-age points while at the same time useful data can be provided with a greatly reduced number of participating smartphones. Furthermore, we show that we can save up to 81 % of energy for communication and sensing while providing inferred readings matching an error bound of 1◦C up to 96 % of the time. As a by-product, our system is privacy-friendly, i.e., it provides data readings of good quality without tracking the position of individual smartphones.

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Fig. 1.Overview of sensing task execution

Evaluation results for our system are discussed in Sect.5. Section6compares our approach to related work while Sect.7concludes this work.

2 System Model and Goals

First, we present our system model and formulate the problem to be solved by our enhanced PS system.

2.1 System Model and Architecture

Following the general design of PS systems, our system consists of two kinds of components: mobile smartphones and a gateway server (see Fig.1). Each smartphone features a positioning sensor such as GPS, has constant Internet access, e.g., via 3G, and has access to a set of environmental sensors (sound, temperature, air pollution, etc.) that may be built-in or connected via Bluetooth. We assume that each smartphone has access to all sensors necessary to satisfy any request posted to the system. Users of mobile smartphones are assumed to be walking with no further assumptions about their mobility. The gateway server, located on the Internet, serves as an interface for clients to request data from the system and redistributes these requests to the smartphones. Note that for scalability the gateway may be implemented as a distributed service.

To request data, clients submit a query Q = (V, p, QoS) to the gateway, consisting of a set of virtual sensors Q.V, a sampling period Q.p, and a set of quality parameters Q.QoS. The sampling period dictates the interval at which readings for all v-sensors should be provided. The quality parameters control the operation of our algorithm and will be explained in the corresponding sections.

Virtual Sensors are attributed with a type of reading v.type and a position

v.loc, thus specifying where to take data readings. Furthermore, each v-sensor has a coverage area v.area deﬁned relative to its location. When a smartphone is located in v.area, it may take a reading forv and we say thatv isavailable. Otherwise,visunavailable. Coverage areas of v-sensors in a queryv∈Q.V must be pairwise disjoint to ensure a unique mapping of smartphones to v-sensors, but may otherwise be chosen arbitrarily.

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is computed at the gateway using a data-driven model without interaction with any device.

2.2 Problem Statement

Our goal is to efficiently provide sensor data on spatially distributed environmen-tal phenomena according to a client-defined quality boundQ.QoS, independent of the current distribution of smartphones in the observed area. We want to minimize the number of requested effective readings while at the same time compensating for unavailable v-sensors and maximize the number of v-sensors

|V′_| _{for which the quality constraints are fulﬁlled.}

3 Optimized Query Execution in Dense Systems

In this section we ﬁrst introduce the multivariate Gaussian distribution model used by our approach. We then present the basic model-driven execution for energy-eﬃcient PS systems (DrOPS), based on [13], that will be extended with adaptive algorithms for compensating for unavailable v-sensors in later sections.

3.1 Multivariate Gaussian Distribution

Multivariate Gaussian Distributions (MGD) have been shown to be a suitable model for inferring values for spatially distributed phenomena, e.g., in [4,5,10]. Their advantage over other methods, e.g., spatial interpolation approaches such as linear interpolation, is that they capture the correlation of observed values rather than relying on indirect criteria, e.g., spatial distance. Note that other types of phenomena, e.g., discrete events, may require a diﬀerent model. In our system, an MGD model is used in two ways: Inferring missing values from a set of incomplete observations and selecting the best set of v-sensors to observe.

Given a model M GDV over a set V of v-sensors and a vector of eﬀective readings Veff at v-sensorsVeff ⊂V, we can infer the most likely current values

µu|PV

eff at (currently unobserved) v-sensorsu∈Vinf=V \Veff as µu|P_V

eff =µu+Σu,VeffΣ −1

Veff,Veff(PVeff −µVeff) (1) σ_u|V2 _eff =σ2u−Σu,VeffΣ

−1

Veff,VeffΣVeff,u (2)

where µV is the vector of mean values for all v ∈ V and ΣV,V is the matrix of (co)variances between all v-sensors in the model. The output is a Gaussian distribution where σ2

u|Veff indicates whether the observations Veff were a good

choice for inferringµu|P_V eff.

To optimize the operation of our system, we strive to minimize the size ofVeff

while ensuring good data quality, i.e., limiting σ_u|W2 to a client-deﬁned thresh-old Q.QoS.σ2max. Finding the smallest Veff that still achieves a given quality

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uncertainty about the values at v-sensors inVinf\ {v} the most. For a detailed discussion of this algorithm and the mutual information criterion, see [8].

To adapt the algorithm to selecting a set of v-sensors based on the requested result quality rather than a predetermined ﬁxed number, we change the termi-nation criterion: in our system, ModifiedGreedy _{adds v-sensors to} _Veff _until ∀u∈Vinf :σ2

u|Veff ≤Q.QoS.σ 2 max.

Note that the achievable degree of optimization depends on the magnitude of the correlations found in the data. If only weak correlations exist, Modified-Greedy_{will select}_Veff ₌_V_{. Furthermore, the accuracy of the selection as well}

as the inference relies on the accuracy of the MGD. As we will show, our system ensures that the MGD in use always reﬂects current data.

3.2 Model-Driven Query Execution

Next, we look at how to apply the model-based optimization in a PS system. The operation of DrOPS is driven by the gateway. Given a query Q = (V, p, QoS), in each sampling period, the gateway creates a sensing task T = (Veff, QoS), Veff ⊆ V as depicted in Fig.1. T is then broadcast to all smart-phones. On receiving T, each smartphone samples its position and determines whether it is located in the coverage area of any v-sensorv∈Veff. If so, it takes a reading of the requested type and returns the reading along with the identity of the v-sensor to the gateway. Should there be more than one eﬀective reading reported for a v-sensor v, only the reading that was taken closest to v.loc is retained. All other readings forv are discarded.

To optimize data acquisition, DrOPS alternates its operation between two phases. In Basic Operation Phases, Veff is equal to V, i.e., no optimization is performed. Data is gathered to build or update an MGD model of the phenom-enon observed in this query and only eﬀective readings for available v-sensors are reported to the client. To keep the optimized operation phase short, an online learning algorithm is used [13]. When an MGD model is available, the system switches to an Optimized Operation Phase. In this phase, ModifiedGreedy

is used to minimize the size of Veff and inferred readings are provided for all v-sensors v ∈ Vinf∪unavailable v-sensors, i.e., where no eﬀective reading was taken. In parallel, an online model validity check algorithm determines whether the current MGD has become inaccurate and if so, switches the system back to a basic operation phase.

4 Alternate Virtual Sensor Selection for Sparse Networks

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Require: V,MGDV,Vunav,Vavl,Q.QoS.σmax2 Veff=Vavl, Vinf=V\Veff

while∃v∈V :σ2_v|V

eff > Q.QoS.σ 2

maxandVeff=V\Vunavdo u=argmaxu∈V_inf\Vunav mutualInformation(u,V,MGDV,Veff) Veff=Veff∪ {u}, Vinf=Vinf\ {u}

end while returnVeff

Fig. 2.AdaptiveGreedy algorithm

sparse network setting, which is characterized by a low probability for each indi-vidual v-sensor to be available. Therefore, we begin by introducing the Adap-tiveGreedy _{algorithm that includes} _knowledge _{about the (un)availability of}

v-sensors in the selection. Finally, we present how to useAdaptiveGreedy_in

ourRound-based Alternate V-Sensor Selection to extend DrOPS to compensate for unavailable v-sensors.

4.1 Adaptive Greedy Algorithm

Compared to the previously described ModifiedGreedy _algorithm, Adap-tiveGreedy _{depicted in Fig.}₂ _{takes two additional parameters: A set of}

v-sensors known to be unavailable Vunav ⊆ V and a set of v-sensors known to be available Vavl ⊆ V, Vavl∩Vunav = ∅. The availability of v-sensors not con-tained in Vavl∪Vunav is unknown. Using an optimistic strategy, AdaptiveG-reedy_assumes _{these v-sensors to be available, although they may turn out to}

be unavailable during task execution. A pessimistic strategy would need to probe the availability of all v-sensors beforehand by querying all smartphones for their position. This would cause the PS system to use as much energy as an approach without any optimization just for probing v-sensor availability, thus voiding the entire optimization approach.

Given these parameters, AdaptiveGreedy _{computes a new selection of}

v-sensorsVeff analogous toModifiedGreedy_{under the additional constraints}

that no v-sensor known to be unavailable is selected and that all v-sensors known to be available are selected, i.e.,Veff∩Vunav=∅andVavl⊆Veff. Forcibly selecting all ofVavlis warranted by the fact that in our system detecting the availability of v-sensorvcoincides with getting an eﬀective reading for v(see Sect.4.2). Thus, not selecting all ofVavl would be a waste of eﬀort.

4.2 Round-Based Alternate Virtual Sensor Selection

We now introduce the Round-based Alternate V-Sensor Selection, depicted in Fig.3, where the gateway subdivides each sampling period into a number of

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Veff,i=Veff,i\ j=1Veff,j ifVeff,i=∅then

return i−1_j=1Ej end if

Ei=executeTask(Veff,i, Q.QoS) end for

return Q.QoS.rounds_j=1 Ej

Fig. 3.Round-based alternate v-sensor selection

round duration should be limited to, e.g., 5 s each to ensure that smartphones cannot move too much between individual rounds. Otherwise, the availability of v-sensors may signiﬁcantly change during each round, thus voiding the knowl-edge on v-sensor availability built so far. For the same reason, we do not carry over knowledge from past sensing periods, as nodes may have moved signiﬁcantly between sensing periods.

In the ﬁrst round, Vavl = ∅ = Vunav, thus we assume all v-sensors to be available. Therefore, the initial selection ofVeff,1is identical to using Modified-Greedy_{as in the non-adaptive system. In fact, when setting}_Q.QoS.rounds_{= 1,}

the system behaves exactly as previously presented in Sect.3. The resulting sub-task T1 is distributed to the smartphones. For all v-sensors in Veff,1 that are actually available an eﬀective readings will be reported to the gateway. All read-ings received in this round are stored in setE1.

In subsequent rounds i = 2. . .Q.QoS.rounds, we ﬁrst update our knowl-edge on v-sensor availability by settingVavl =Vavl∪Ei−1 andVunav =Vunav∪

(Veff,i−1\Ei−1). Thus, all v-sensors for which a reading was requested but no eﬀective reading was received are known to be unavailable for the remainder of the sampling period. Based on this new knowledge we then compute a new selectionVeff,iusingAdaptiveGreedy_{. A new subtask}_Ti_{= (}_Veff_,i\i−1

j=1Veff,j)

is then distributed to the smartphones. We repeat this process until either the maximum number of rounds has been reached or no additional v-sensors were selected. At this point, inferred readings are computed from all eﬀective readings that have been collected.

5 Evaluation

We evaluated our approaches based on real-world environmental measurements and generated mobility traces. In the following, we will ﬁrst present the setup of our evaluation before discussing the results in detail.

5.1 Simulation Setup

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measurements: LAB data from 50 fixed sensors deployed in an indoor lab [5] and LUCE data from over 100 fixed sensors from an outdoor deployment [11]. Using real-world data readings is important to make the performance of the model-driven optimization comparable to that of a real deployment of our system, i.e., to observe realistic correlations of individual v-sensors. Queries are generated by replicating the fixed sensors of each data set as v-sensors in order to generate a temperature map of the observed area. For our PS system, we generated mobil-ity traces for a varying number of smartphones, following the available paths in each deployment area. Energy cost is modeled using empirical energy models for communication [2] and sensing [14]. We do not consider energy for positioning, as it is amortized over other location-based applications frequently running on a smartphone. Each simulation runs for 6 simulated hours with a time offset between simulations increasing in steps of 3 h from the start of each data set. Quality parameters are set to Q.QoS.σmax2 = 0.1 for the AdaptiveGreedy

algorithm and Q.T = 1◦_{C as an absolute acceptable error threshold for the}

model validity check algorithm.

We analyze the performance of our system under three metrics:Quality, Bro-ken Queries, and Relative Energy Consumption. We compare the performance of our system to a naive algorithm without optimization, i.e.,Veff =V always, and the original DrOPS system for dense networks.

5.2 Quality

The Quality metric, depicted in Fig.4, is deﬁned as the fraction of queries in which the QoS-constraints are met out of all queries for which at least one eﬀective reading was received, thus characterizing the data quality a client can expect from the system. Values are averaged over all simulation runs for each number of mobile smartphones.

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approach, the quality increases to over 90 % in a dense system. Furthermore, it is far more robust to a decreasing number of smartphones. In the LUCE data, for example, using 3 rounds we can still provide 81 % quality using 50 smartphones, whereas using DrOPS requires 400 smartphones to match this quality.

5.3 Broken Queries

Next, we analyze results for the broken queries metric, denoting the fraction of queries for which no eﬀective readings were received at the gateway, i.e., characterizing how both approaches perform at ﬁnding available v-sensors.

Evaluation results are depicted in Fig.5. Again, values are averaged over all simulation runs for each number of smartphones. Similar to the quality metric, the number of broken queries using DrOPS drastically increases for a decreasing number of smartphones, while our extended algorithm is much more robust. Under the LAB data, for a single round the fraction of broken queries increases to 5 % for 140 smartphones whereas using 3 rounds, we can provide 7 % of broken queries with only 40 smartphones. For the LUCE data, DrOPS cannot match the fraction of broken queries when using 3 rounds and at least 100 smartphones.

5.4 Relative Energy Consumption

Finally, we use the relative energy consumption (REC) metric to characterize the energy consumption. As the absolute energy consumption varies greatly for diﬀerent time oﬀsets, e.g., due to a varying number of sensing tasks, the REC is computed by normalizing the energy consumption for each node in a simulation

0 %

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20 % Fig. 6.Cumulated relative energy consumption, LAB data

by the average energy consumption per node using the naive algorithm for the same simulation parameters. Figure6 shows the cumulated average REC per simulation for the LAB data. Results for the LUCE data are similar and thus not shown due to space constraints. Note that the energy drain is nearly uniformly distributed among all nodes in a simulation. The maximum diﬀerence between nodes in a simulation was 14.4 percentage points.

We see that additional communication for additional rounds increases the energy consumption. The difference is greatest in a sparse setting, where few effective readings are collected in early rounds, i.e., most work is done in later rounds. In a denser setting, the difference diminishes, as later rounds add fewer readings and thus less energy is used in later rounds. Note the sharp increase in REC for DrOPS in Fig.6a. For about 90 % of simulations, hardly any data is collected, i.e., only few available v-sensors are found and thus little energy is spent (cf. Figs.4a, 5a), whereas for the few cases where available v-sensors are found, only a weak model can be derived, i.e., Veff is very large. As the round-based approach is better at finding available v-sensors, it does not exhibit this behavior. Using our round-based approach, we still can save up to 77 % of energy (compared to 81 % for DrOPS). When the system contains at least as many smartphones as v-sensors, energy consumption is at most that of the naive approach. When fewer smartphones are present, the round-based approach may use up to 6 % more energy.

Mobile and Ubiquitous Systems Computing. Networking, and Services

123

Ivan Stojmenovic

Zixue Cheng

Song Guo

(Eds.)

Mobile and Ubiquitous

Systems: Computing,

Networking, and Services

10th International Conference, MOBIQUITOUS 2013

Tokyo, Japan, December 2–4, 2013

Revised Selected Papers

Editorial Board

Mobile and Ubiquitous

Systems: Computing,

Networking, and Services

10th International Conference,

MOBIQUITOUS 2013

Tokyo, Japan, December 2

–

4, 2013

Revised Selected Papers

Organization

Steering Committee

Organizing Committee

Technical Program Committee

Contents

·

·

1

Introdution

2

Running Example

3

System Overview

4

Situation Inference Engine Implementation

5

Experimental Evaluation

6

Related Work

7

Conclusion

References

1

Introduction

2

System Model and Goals

3

Optimized Query Execution in Dense Systems

4

Alternate Virtual Sensor Selection for Sparse Networks

5

Evaluation

_–