A fast and scalable approach for IoT service selection ...

Inf Syst Front DOI 10.1007/s10796-016-9650-1

A fast and scalable approach for IoT service selection based on a physical service model Xiongnan Jin 1 & Sejin Chun 1 & Jooik Jung 1 & Kyong-Ho Lee 1

# Springer Science+Business Media New York 2016

Abstract Information Systems (ISs) have become one of the crucial tools for various organizations in managing and coordinating business processes. Now we are entering the era of the Internet of Things (IoT). IoT is a paradigm in which realworld physical things can be connected to the Internet and provide services through the computing devices attached. The IoT infrastructure is starting to be integrated with ISs thereby diminishing the boundaries between the physical world and the business IT systems. With the development of IoT technologies, the number of connected things and their available physical services are increasing rapidly. Thus, selecting an appropriate service that satisfies a user’s requirements from such services becomes a time-consuming challenge. To address this issue, we propose a Physical Service Model (PSM) as a common conceptual model to describe heterogeneous IoT physical services. PSM contains three core concepts (device, resource, and service) and specifies their relationships. Based on the proposed PSM, we define three types of Quality of Service (QoS) attributes and rate candidate services according to user requirements. To dynamically rate QoS values and select an appropriate physical service, we

* Kyong-Ho Lee [email protected] Xiongnan Jin [email protected] Sejin Chun [email protected] Jooik Jung [email protected] 1

Department of Computer Science, Yonsei University, Seoul, Republic of Korea

propose a Physical Service Selection (PSS) method that takes a user preference and an absolute dominance relationship among physical services into account. Finally, experiments are conducted to evaluate the performance of the proposed method. Keywords Internet of things . Physical service model . Physical service selection . Absolute dominance relationship

1 Introduction Recently, Information Systems (ISs) have become one of the crucial tools for various organizations in managing and coordinating business processes. These systems are heavily utilized by many system engineers to store a wide variety of information and allow inter-communication between management hierarchies. The applications of ISs have covered domains such as business analytics (Duan and Xu 2012), information architecture for supply chain management (Xu 2011; Gao et al. 2013), integrated medical supply systems (Xu et al. 2011), automated assembly planning system (Xu et al. 2012), service workflow management (Monakova and Leymann 2013), humanmachine system design (Yin et al. 2012), environmental monitoring (Fang et al. 2014), and IT ventures (Li et al. 2015b). Now we are entering the era of the Internet of Things (IoT). In the IoT paradigm, the capabilities of identifying, sensing, actuating, and networking can be attached to real-world physical things to make them accessible through the Internet (Haller et al. 2013) with the help of the computing devices such as RFID tags, sensors, actuators, smartphones, computers, etc. Billions of physical things will be interconnected, providing and consuming mass information with different scales and different structures on various networks.

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As suggested in many literatures, IoT seamlessly connects a variety of heterogeneous things for end-users to easily share and efficiently access the information of interest. Consequently, IoT is expected to be a significant technological leap in human society similar to the Internet, which has played a vital role in the past decades. In order to promote interoperability between various physical entities in IoT, the service-oriented approach has been suggested as a possible solution by many researchers. This approach must not be overlooked since the IoT infrastructure is starting to be integrated with ISs thereby diminishing the boundaries between the physical world and the business IT systems. In fact, IoT is changing almost all aspects of business IT systems including how we manage and use business services. The potentialities offered by IoT enable the development of a large number of applications (Atzori et al. 2010). IoT applications may improve the quality of our lives in various domains, including healthcare (Rabbi et al. 2015; Rahman et al. 2015; Sun et al. 2015; Lane et al. 2015), supply chains (Qiu et al. 2015; Gnimpieba et al. 2015), social network services (Girau et al. 2013; Jung et al. 2015), smart environments (Funk et al. 2015; Nakamura et al. 2015; Jin et al. 2015), etc. There are also commercial or open source IoT service platforms include Evrythng,1 Xively,2 Carriots,3 and Compose.4 These service platforms enable the creation of innovative applications and the combination of data collected from physical things in various application domains (Hur et al. 2015). Each platform has different data structure and data representation. This heterogeneity among IoT service platforms may cause a challenging interoperability problem. Semantic Web technologies have emerged as a means to solve the interoperability problem in IoT. The SSN (Semantic Sensor Network) ontology is proposed to describe sensors and their data in terms of capabilities, measurement processes, observations and deployments (Compton et al. 2012). Additionally, IoT models are presented to define the main concepts of the physical world and their relationships as a common lexicon and taxonomy (Haller et al. 2013). However, existing models may not describe physical services elaborately in terms of spatio-temporal aspects. The spatiotemporal properties should be considered as key factors when selecting physical services. IoT provides physical services for various purposes such as sensing and actuating different things (e.g., providing information about or acting on things). Physical services expose the resources of things through a uniform service interface and make resources available for other devices and services. With 1

https://evrythng.com https://xively.com 3 https://www.carriots.com 4 https://www.compose.io

the development of IoT technologies, the number of connected things and their physical services are increasing rapidly. Cisco’s Internet Business Solutions Group5 predicts that 50 billion things will be connected to the Internet by 2020. Consequently, the number of physical services will be larger as physical things may provide one or more services. Selecting appropriate physical services that satisfy a user’s requirements among a huge number of candidate services therefore becomes a challenging and time-consuming task. A number of service selection methods have been proposed in a Web service domain (Wu et al. 2009; Yu and Bouguettaya 2013; Chan et al. 2006; Zhang et al. 2013; Agarwal and Jalote 2010; Han et al. 2012; and Jang et al. 2006). Due to several differences between the conventional Web services and physical services, however, traditional Web service selection methods are incompatible with physical services. For instance, IoT objects and things providing physical services are often resource-limited, while Web services are usually hosted on powerful computers with sufficient computing and networking capabilities. In addition, physical services tend to be mobile and have more uncertainty of network availability, while Web services are relatively static and reliable. In this paper, we propose a Physical Service Model (PSM) as a conceptual model of describing physical services. The proposed PSM takes devices, resources, and services as three core concepts and specifies relationships among them. In addition, PSM describes physical services in terms of spatiotemporal features that are important factors in an IoT domain. Based on PSM, we define three types of Quality of Service (QoS) attributes (spatio-temporal, positive, and negative), which reflect the features of physical services. In addition, we propose a Physical Service Selection (PSS) method of aggregating and evaluating the QoS values of Candidate Physical Services (CPSs) based on a user preference. The PSS algorithm is mainly composed of three phases: pre-sorting, filtering, and final sorting. Finally, we conduct experiments to evaluate the performance of the proposed approach. Experimental results show that our approach is efficient in selecting physical services among a large number of CPSs. The rest of this paper is organized as follows. In Section 2, we give an overview of IoT and summarize related works. The proposed PSM is introduced in Section 3. The description of the QoS rating and PSS method is given in Sections 4 and 5. In Section 6, experiments are conducted to evaluate the performance of the proposed approach. Finally, we conclude and give some outlook on future work in Section 7.

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http://share.cisco.com/internet-of-things.html

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2 IoT overview and related works In this section, we give an overview of IoT in terms of definition, promise, applications, and platforms. In addition, related works on service models and service selection approaches are summarized. 2.1 An overview of IoT The concept of IoT was first proposed by Kevin Ashton in 1999 and was described as uniquely identifiable interoperable connected objects with the radio-frequency identification (RFID) technology. Although there is no unified definition of IoT, IoT is generally defined as Bdynamic global network infrastructure with self-configuring capabilities based on standards and interoperable communication protocols; physical and virtual things in an IoT have identities and attributes and are capable of using intelligent interfaces and being integrated as an information network^ (IERC 2013; Kirtsis 2011). IoT aims at building a global network of things to support ubiquitous computing (Bandyopadhyay and Sen 2011; Broll et al. 2009) and context-awareness among devices (Dong et al. 2010; Garrido et al. 2010). Ubiquitous computing and context-awareness are important components of ambient intelligence. Ambient intelligence allows physical things to comprehend their surroundings, make decisions accordingly, and interact with people (Whitmore et al. 2014). Our life and society would benefit from numerous smart things in IoT. IoT makes it possible to collect, store, and transmit the information about or generated by the things equipped with tags or sensors (Li et al. 2015a). Tags and sensors are widely used in supply chain management, manufacturing, environmental monitoring, retailing, smart shelf operations, healthcare, food and restaurant industry, logistic industry, travel and tourism industry, library services, product life-cycle energy management and many other areas (Bi et al. 2014; Cai et al. 2014; Fang et al. 2015; Xu et al. 2014; Tao et al. 2016; Pencheva and Atanasov 2014; Pang et al. 2015). Here, to discuss the potential and impact of IoT on our life and society, we introduce some applications in the domain of healthcare, supply chains, social network services, and smart environments. 2.1.1 Healthcare Healthcare is an important application area of IoT (Cai et al. 2014; Yin 2016). A number of medical sensors and wearable devices are attached to people to collect health information. MyBehavior is a smartphone application that automatically learns a user’s physical activity and dietary behavior and strategically makes suggestions to those behaviors for a healthier lifestyle (Rabbi et al. 2015). DoppleSleep provides a single sensor solution to track sleep-related physical and

physiological variables including coarse body movements and subtle and fine-grained chest movements due to breathing and heartbeat (Rahman et al. 2015). In addition, SymDetector is a smartphone based application to unobtrusively detect the sound-related respiratory symptoms occurred in a user’s daily life, including sneeze, cough, sniffle and throat clearing (Sun et al. 2015). DeepEar is the first mobile audio sensing framework built from coupled Deep Neural Networks that simultaneously perform common audio sensing tasks (Lane et al. 2015). Furthermore, a wearable wireless sensor network is proposed for the anomaly detection of health conditions (Yan et al. 2015). 2.1.2 Supply chain Wireless sensor networks and RFID have already played roles in supply chains. As we all know, various sensors have been utilized in assembly lines and RFID is often used to track products. To provide public logistics services, an IoTenabled supply hub in an industrial park is proposed to enhance the effectiveness and efficiency of sharing physical assets and services (Qiu et al. 2015). Besides, a collaborative cloud-based platform is proposed to support the data sharing, integration and processing for logistic goods tracking and tracing (Gnimpieba et al. 2015). 2.1.3 Social network service Social Internet of Things is a new paradigm which aims to integrate the IoT with human-centric social networks to capture various interactions between humans and physical things. An implementation of a SIoT platform is presented, where objects can create their own relationships, create groups and produce and consume services (Girau et al. 2013). Besides, a network-based multidimensional structure is proposed to model SIoT for efficient management and discovery of IoT objects (Jung et al. 2015). 2.1.4 Smart environment A smart environment is becoming an important part of our lives thanks to IoT and its potential to integrate Bsmartness^ with physical things. (Atzori et al. 2010). A mobile cameraprojector cart called OrderPickAR, which combines the benefits of both stationary and mobile systems, is presented to support order picking through Augmented Reality (Funk et al. 2015). In addition, a smart home system, called Smart Life Support Agent (SLSA), is proposed to generate smart life tips by recognizing the customary activities of residents in homes (Nakamura et al. 2015). On the other hand, there exist several famous IoT service platforms, including above mentioned Evrythng, Xively, and Carriots. Evrythng is an IoT platform that connects products

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to the Web to make them smart, interactive and traceable. Xively is a connected product management solution that provides companies with ability to build, launch and run their connected products and business. Additionally, Carriots is designed as a Platform as a Service (PaaS), specifically targeted for the IoT and Machine to Machine (M2M) projects. 2.2 Related work on service modeling and selection The Web Ontology Language for Services (OWL-S) is a service description framework that provides rich expressive descriptions and well-defined semantics for Web services (W3C 2004). OWL-S has three main parts: a service profile for advertising and discovering services, a process model that gives a detailed description of a service’s operation, and a grounding that provides details on how to interoperate with a service via messages. OWL-S cannot describe, however, spatio-temporal and heterogeneous features of physical services. In an IoT domain, there are some works that intend to model physical services. The IoT domain model (Haller et al. 2013) provides a common lexicon and taxonomy and defines main concepts and their relationships. There are five main concepts in the model, which are augmented entity, user, device, resource, and service. Additionally, in our previous work (Chun et al. 2014a), we extend the IoT domain model with the event concept to express various events that represent contextual changes within the IoT environment. A semantic modeling approach (De et al. 2011) is proposed for various components in an IoT framework. The semantic modeling consists of three core concepts, which are entity, resource, and service, and describes their relationships. These models, however, lack sufficient spatio-temporal features. Spatio-temporal properties are key factors in physical service selection. The Incubator Group on Semantic Sensor Networks of W3C has further introduced the SSN ontology (Compton et al. 2012) that describes sensors in terms of capabilities, measurement processes, observations, and deployments. Both the OWL-S service profile and the SSN ontology are considered in our PSM to describe a service and a device. A QoS profile plays a central role in research on Web service selection (Moghaddam and Davis 2014). A rating method called ServiceRate (Wu et al. 2009) considers QoS aspects such as response time, availability, and the social perspectives of services. Another QoS model is proposed by Agarwal and Jalote (2010). Their model includes QoS attributes such as cost, response time, availability, throughput, and reputation. A trust evaluation model based on a trust cloud and a user capability is proposed for electronic learning (Tan et al. 2014). To get an aggregated rating, conventional approaches calculate the weighted sum of individual QoS ratings using a

normalized weight for each attribute that specifies a user preference. It is a rather challenging task, however, for users to transform their preferences into numeric weights (Yu and Bouguettaya 2013). We therefore take a user preference in the order of priority rather than a numeric format. With the number of candidate services increasing, the time it takes to carry out service selection is also growing. Some researchers try to solve the problem by a dominance relationship among services to reduce the number of candidate services (Yu and Bouguettaya 2013; Chan et al. 2006; Zhang et al. 2013; and Alrifai et al. 2010). Given a set of multidimensional points, point p is said to dominate another point q if it is better than or equal to q in all dimensions and better than q in at least one. Only skyline services (Chan et al. 2006), which are the subset of candidate services that are not dominated by other services, are selected to reduce the search space. The exact computing of skyline services, however, costs an increasing amount of time as the number of candidate services increases. We therefore define an absolute dominance relationship among physical services. A physical service p absolutely dominates another physical service q when the minimum individual QoS rating of p is greater than the maximum individual QoS rating of q. Services that are absolutely dominated by another service are therefore deleted from the candidate set to reduce the number of CPSs. In our previous work, we propose a service selection method for IoT services based on physical service model and absolute dominance relationships among IoT services (Jin et al. 2014). However, the QoS attributes are limited to available time, service area, processing time, and reputation. This restricts the applicability of the approach. In this paper, we categorize QoS attributes into three types to address more common QoS-aware service selection problems. Specifically, each QoS type can have multiple QoS attributes. In addition, the quality rating method for the three types of QoS attributes is presented and the physical service selection algorithm is improved accordingly. Furthermore, additional experiments in terms of filtering, overall selection, and pre-sorting are conducted in comparison with the conventional approaches.

3 Physical service model To describe physical services in terms of spatio-temporal properties, we propose PSM as a conceptual model of describing physical services, which contains three core concepts and their relationships. PSM can be used as a reference dictionary by various IoT platforms, directories, and middleware to uniformly register physical services. After that, reasoning technologies can be applied to answer search queries. The definitions of the three core concepts are as follows.

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Device: Devices connect entities to the Internet. Entities are real-world objects, e.g. cows on a farm or a laboratory in a building. Devices provide services and may indicate their temporal availability. Resource: Resources provide access to information about, or actuation capabilities for, entities. Furthermore, resources have a spatial feature that indicates the working range of their hosting devices. Service: Services expose resources through a common interface and make them available on the Internet. In addition, services have service areas and times, which are inferred from the spatio-temporal features of their corresponding resources and devices.

Three core concepts are described in detail in following subsections. Note that, all URIs used in PSM are in the form of the Semantic URIs (S-URIs) (Chun et al. 2014b). The SURI scheme is proposed to promote meaningful identification, discovery, and integration of things across different domains. Furthermore, an S-URI promotes an easy exploration of information about objects and provides both human- and machine-interpretable schemes. 3.1 Device model A device is a piece of computing hardware attached to a realworld entity (isAttachedTo) and is owned by a user (hasDOwner). A user is described by Friend of a Friend (FOAF6) ontology. The FOAF ontology describes relationships among people and information on the Web. In addition, a device hosts a resource (hosts) and may provide, invoke, or subscribe to a service. A device has particular features and properties, such as a TemporalFeature (hasDTF), SpatialFeature (hasDSF), DeviceName (hasDName), Mobility (hasMobility), Identifier (isIdendifiedBy), and DeviceType (hasDType). For the TemporalFeature, a device first has an AvailableTime that is specified through TimeZone, RangeOfDate, and RangeOfTime. A RangeOfDate is defined in terms of a start date and an end date, while a RangeOfTime is composed of a start time and an end time. Second, a device has a DeploymentLocation for its SpatialFeature and the DeploymentLocation is specified through GlobalLocation and LocalLocation. Specifically, GlobalLocation is described by geographical coordinates (longitude, latitude) or by the GeoNames ontology.7 Third, a device has properties such as DeviceName and Mobility. The value type of Mobility is Boolean, which indicates whether a device is a mobile device. Fourth, a device is assigned an S-URI as a unique identifier. Fifth and finally, a DeviceType is divided into tag, actuator, sensor, etc. In the case of the sensor type, the SSN ontology is 6 7

http://xmlns.com/foaf/spec/ www.geonames.org/ontology/

employed to describe sensors. Figure 1 illustrates in a UML notation the main concepts and their relationships to the device model. 3.2 Resource model A resource is a computational element hosted on a device (isHostedOn). In addition, a resource is exposed by a service through a common interface (isExposedBy) and provides information about or acts on an entity (hasInformationAbout/ actsOn). An S-URI is assigned to a resource (isIdendifiedBy). ResourceType (hasRType), which is related to DeviceType, consists of identifying, actuating, and sensing resources. A resource also has a SpatialFeature (hasRSF), which indicates the WorkingRange of its hosting device. WorkingRange is specified through the GlobalRange and LocalRange concepts. The GlobalRange concept is composed of the GlobalLocation of a hosting device and its radius. Meanwhile, LocalRange is described by a local ontology, which is typically domain-specific. Figure 2 shows the main concepts and their relationships to the resource model in a UML notation. 3.3 Service model A service exposes a resource (exposes) through a common interface (hasInterface) and makes it available on the Internet. The interface type is divided into Representational State Transfer (REST) (Fielding and Taylor 2002), SOAP (W3C 2007), etc. In addition, a service has certain features and properties, such as ServiceType (hasSType), AvailableTime (hasSTime), WorkingRange (hasSArea), and Identifier (isIdentifiedBy). To describe the model in detail, ServiceType is first inferred from the ResourceType concept (isInferredFrom). It consists of identifying, actuating and sensing services. Second, the AvailableTime concept is related to the available time of a device, and indicates the service time. Similarly, WorkingRange indicates the service area that is related to the working range of its resources. Third, a service has the service properties of Input (hasInput), Output (hasOutput), Precondition (hasPrecondition), Effect (hasEffect), and ServiceName (hasSName). In the case of sensing services, an output is the property of interest to a user. In some cases, a service needs an input to a function. In contrast, an effect plays an important role for actuation services, with precondition. Another important concept in the service model is a role (isAccessibleBy), which is played by a user (hasRole). For instance, healthcare-related services may expose private information (e.g. blood pressure), which a service provider wants to share only with certain people. Such users are allowed to access a service depending on their roles. Finally, a profile is used to describe the conventional QoS attributes such as processing time, reputation, etc. Figure 3

Inf Syst Front Fig. 1 Device model

DeviceType

User hasDOwner

hasDType

hosts

Resource

Enty isAachedTo provides

Device

Service

invokes/subscribes hasDName

DeviceName

hasMobility

Mobility

hasDTF

hasDSF

TemporalFeature

SpaalFeature

AvailableTime

DeploymentLocaon

has

TimeZone

illustrates the main concepts and their relationships to the service model in a UML notation. PSM extends existing models with spatio-temporal properties. Specifically, the AvailableTime of TemporalFeature and DeploymentLocation of SpatialFeature in the device model, the WorkingRange of SpatialFeature in resource model, and their relationships with the service model are proposed to describe physical services elaborately.

RangeOfDate

isIdenfiedBy

RangeOfTime

Idenfier

has

GlobalLocaon

LocalLocaon

Besides spatio-temporal attributes, which are specialized for physical services, the conventional QoS attributes of Web services are still useful and considered in our physical selection approach. The conventional QoS attributes are divided into negative and positive attributes. &

Negative attributes: Negative attributes are attributes for which a lower value is preferred. The following processing time and execution cost are two instances of negative attributes.

Based on the proposed PSM, we define three types of QoS attributes, which reflect the features of physical services.

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Spatio-temporal attributes: Physical services tend to be mobile and are not always available due to some issues of network, energy provision, or privacy. The spatiotemporal properties make the selection of physical services different from that of Web services. The two kinds of spatio-temporal attributes include available time and service area.

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Processing Time (PT): The PT of a physical service is the expected delay between the moment when the physical service is invoked and the arrival time of a response. Execution Cost (EC): A service requester must pay for the invocation of a physical service to get a result.

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Positive attributes: Positive attributes include reputation and reliability, for which a higher value is preferred. Reputation and reliability are two kinds of positive attributes.

Available Time (AT): The AT of a physical service denotes the time when a service provider delivers a service. AT consists of [ats , ate ], where ats indicates the start time and ate indicates the end time of a physical service. In addition, AT is related to the TemporalFeature of a device in PSM. Service Area (SA): The SA of a physical service indicates the location where a physical service observes or actuates. SA is specified with either a set of a central point and a radius, or a location concept (e.g. from the GeoNames8 ontology). Additionally, SA is related to the SpatialFeature of a resource in PSM.

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Reputation (RP): After a user (device or service) requests physical services, the user rates physical services based on their performance. The RP of a physical service is calculated according to the ratings it has gained. Reliability (RE): The RE of a physical service is the probability that the physical service will correctly respond to a request within the expected time.

4 Quality rating of physical services

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http://www.geonames.org/ontology/documentation.html

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Note that the attributes belonging to each type are not limited to the above mentioned six attributes and may easily be extended. In the rest of the section, we describe the proposed method of rating the three types of QoS attributes of CPSs in detail.

Inf Syst Front Fig. 2 Resource model

Enty

Service

hasInformaon About / actsOn

isExposedBy

isHostedOn

Device

Resource

hasRType

isIdenfiedBy

hasRSF

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has

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4.1 Rating spatio-temporal attributes 4.1.1 Available time It would be optimal to find a physical service whose available time fully matches the time requested by a user. In reality, however, a user may have to compromise and select a similar one. The request time of a user may be a moment in time or a range of time. The available time of physical services is specified in a service profile by a service provider. It is associated with the temporal features of devices. The rating of an available time, ATi; j , is indicated by the degree to which the request time of user i matches the available time of service j and is computed as follows.

ATi; j

8 1; if available time includes the request moment > < jmatchingTimeði; jÞj ¼ ; if the request time is a range of time > : jrequestTimgRangej 0; if available time does not include the request moment

ð1Þ

In Eq. (1), if the available time and the request range of time fully match or the available time includes the moment in time that is requested, ATi; j equals 1. If there is no matching range of time or the available time does not include the moment in time of the request time, ATi; j equals 0. Otherwise ATi; j ranges from 0 to 1. Figure 4 shows the example of rating available time. The request time range of user i is from 8:10 to 8:20, 10 min of total request time, and the available times of three services are demonstrated. Service1 has a matching time of 5 min, so the ATi;1 comes out to be 0.5(=5/10). For Service2, the matching time is 10 min, fully matching, thus ATi;2 equals 1. For Service3, matching time is 7 min, so the ATi;3 equals to 0.7(= 7/10). 4.1.2 Service area In an IoT environment, a user may request physical services, which are available at a specified location. The service area

Fig. 3 Service model ServiceType

AvailableTime has Available Time

Device

isAccessibleBy

Role

hasRole

User

isInferred From

hasRType

ResourceType

hasSType has SArea

hasSTime isProvidedBy isInvoked/ SubscribedBy

Service

isIdenfiedBy

Profile

hasInput

Resource

exposes

hasProfile

Input

has RSF

WorkingRange

hasInterface

Idenfier

hasOutput

Output

Interface

hasPrecondion

Precondion

hasEffect

Effect

hasSName

ServiceName

Inf Syst Front Fig. 4 An example case of rating available time

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specified in a service profile is associated with the spatial features of resources. The rating of a service area is indicated by the distance between the request area of user i and the service area of service j, denoted by SAi; j, and is computed as follows. ( SAi; j ¼

1−

distanceði; jÞ ; if distanceði; jÞ is less than d d 0; if distanceði; jÞ is greater than d ð2Þ

As shown in Eq. (2), a service with a service area that is closer to the requested area gains a higher rating. But if the distance is larger than threshold d, SAi; j equals 0. Distance (i, j) can be simply calculated by the geographical distance. Figure 5 shows an example of rating a service area. A request and a service area are shown, and the distance threshold is set to be 10. Since the distance of Service1 has the value of 2, SAi;1 comes out to be 0.8(= 1–2/10). For Service2 with the distance 5, thus SAi;2 becomes 0.5(= 1–5/10). For Service3, whose distance is larger than the threshold, SAi;3 equals 0.

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4.2 Rating negative attributes The negative attributes (processing time and execution cost) are also specified in a service profile by a service provider. Generally, a physical service with a lower value of a negative attribute should be rated higher. Similarly, a utility function is applied to rate a negative attribute of physical service j, denoted by N A j. The N A j rating is computed as follows. NAj ¼

namax − na j namax − namin

ð3Þ

In Eq. (3), namax indicates the maximum negative attribute value of a CPS, while namin indicates the minimum negative attribute value. In addition, na j is the negative attribute value of physical service j. 4.3 Rating positive attributes Positive attributes such as reputation and reliability are specified in a service profile by a service provider. In general, a physical service with a higher value for a positive attribute should be rated higher. We use a utility function to rate a

Fig. 5 An example case of rating service area









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positive attribute of physical service j, denoted by PA j. The PA j rating is computed as follows. PA j ¼

pa j −pa pamax − pamin min

Table 1 An example of pre-sorted candidate physical services

ð4Þ

In Eq. (4), pamax indicates the maximum value of a CPS, while pamin indicates the minimum value. In addition, pa j is the positive attribute value of physical service j.

5 Physical service selection To select an appropriate physical service, the individual QoS ratings calculated in the previous section must be aggregated. The proposed PSS is based on a user preference and an absolute dominance relationship among physical services. For simplicity purposes, in this section we consider only four attributes (AT, SA, PT, and RP). More attributes including EC and RE may be added easily and will be discussed in the following section. We take a user preference in the order of priority, e.g., [AT, SA, PT, RP], which indicates that AT is the most preferred QoS attribute and RP is the least preferred. A user preference is used to sort CPSs. In our approach, four QoS ratings are considered after being sorted, which may result in a high value in time complexity. To address this limitation, a pre-sorting method is applied. Since the AT and SA ratings are sensitive to user requirement, they should be processed at run-time. Meanwhile, the PT and RP ratings are not user-requirement sensitive. Thus, the PT and RP ratings are used to pre-sort CPSs at design time. The pre-sorting method is described in detail as follows. Suppose that CPSs = {s1, s2, …, sn}, where si, 1 ≤ i ≤ n, indicates a physical service, and the individual QoS ratings of physical service si are represented with (si .AT, si.SA, si.PT, si.RP). In addition, PT and RP ratings are computed at design time and CPSs are pre-sorted based on the PT and RP ratings. Both results are kept for further sorting at run time. Here, an example is presented to illustrate the pre-sorting method. In Table 1, all the AT and SA ratings are equal to 0 since they cannot be computed at design time. Each result, based on PT or RP ratings, is kept for the final sorting. The AT and SA ratings of CPSs are then computed at run time. According to the user preference, the AT and SA ratings are added to the corresponding pre-sorting result. i.e., if PT is more preferred to RP, then AT and SA ratings will be added to the PT rating based pre-sorting result. Otherwise, AT and SA ratings will be added to the RP rating based pre-sorting result. Here, an example is presented to show the physical services with full QoS ratings. In Table 2, with user preference [AT, PT, SA, RP], in which PT is more preferred to RP, the AT and SA ratings are added to the PT rating based on the pre-sorting result. In the second case, AT and SA ratings are added to the one based on the RP rating.

Based on PT rating CPS AT SA 0 0 S3 0 0 S2 0 0 S5 0 0 S1 0 0 S4

PT 0.90 0.87 0.85 0.73 0.53

RP 0.46 0.83 0.29 0.92 0.44

Based on RP rating CPS AT SA 0 0 S1 0 0 S2 0 0 S3

PT 0.73 0.87 0.90

RP 0.92 0.83 0.46

0.53 0.85

0.44 0.29

S4 S5

0 0

0 0

As the number of CPSs increases, however, the processing time also increases. Therefore, a filtering method using an absolute dominance relationship among physical services is applied. In our definition, physical service p absolutely dominates another physical service q when the minimum individual QoS rating of p is greater than the maximum individual QoS rating of q. The physical services that are absolutely dominated by another physical service are then deleted from the candidate set to reduce the number of CPSs. For instance, the maximum individual QoS rating of s4 (s4.PT = 0.53) is less than the minimum individual QoS rating of s2 (s2.RP = 0.83), i.e., s4 is absolutely dominated by s2, thus s4 is deleted from CPSs. After the procedure, final sorting is performed based on the user preference in descending order. Algorithm 1 describes the entire process of the proposed PSS method. In steps 2–4, the individual QoS ratings of PT and RP are computed and the CPSs are pre-

Table 2 An example of physical services with full QoS ratings

User preference: [AT, PT, SA, RP] CPS AT SA PT 0.59 0.67 0.90 S3 S2

0.92 0.90 0.87 0.59 0.46 0.85 S5 0.82 0.25 0.73 S1 0.46 0.48 0.53 S4 User preference: [RP, SA, AT, PT] CPS AT SA PT 0.82 0.25 0.73 S1 0.92 0.90 0.87 S2 0.59 0.67 0.90 S3 0.46 0.48 0.53 S4 0.59 0.46 0.85 S5

RP 0.46 0.83 0.29 0.92 0.44 RP 0.92 0.83 0.46 0.44 0.29

Inf Syst Front

sorted by PT and RP. Steps 5–10 perform the computing of the individual QoS xratings for AT and SA according to user requirements. Based on the user preference, AT and SA ratings are then added to the pre-sorted results. In

steps 11–18, the physical services absolutely dominated by another physical service are deleted from the candidate set. The remaining steps execute the final sorting and the selection according to the user preference.

Algorithm 1. Physical Service Selection Require: user preference UP = [up1, up2, up3, up4], user requirements UR, CPSs S = {s1, s2, …,sn} 1: begin 2: rating(PT,RP), addRatingInfo(PT,RP); 3: PSPT = pre-sorting(PT); 4: PSRP = pre-sorting(RP); 5: input UP, UR; 6: ratingBasedOnUR(AT,SA); 7: if UP.PT > UP.RP 8: then FS = addRatingInfoToPSPT(AT,SA); 9: else FS = addRatingInfoToPSRP(AT,SA); 10: end if 11: for all si in S do 12: maxRating(si), minRating(si); 13: end for 14: minMax = maxValue(minRating(si)); 15: for all si in S do 16: if maxRating(si) xsd:time('10:00:00') && xsd:time(?endtime)