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Computer Science
Mathematics

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An Energy Efficiency policy for Communications with C-RAN, ICN and Transition Smooth

Towards next generation communications, Energy Efficiency (EE) attracts lots of attentions nowadays. Some innovative techniques have been proposed in prior literatures, especially the sleep mechanism of base station (BS). Yet how to sleep and when to sleep are still vague concepts. Another, most of the studies focus on the cellular section or core networks separately while integral and comprehensive version is neglected in prior literatures. In this paper,the integral optimization structure is studied based on cloud radio network (C-RAN) and information centric network (ICN) that raised latest combined with the sleep mode. The original C-RAN and ICN structures are amended in terms of reality application of sleep techniques. While adopting the sleep techniques both in core and cellular, apart from previous works, a transition smooth method that solve the current surge problems which is ignored before is further proposed. Based on the new method, it will be much more feasible to adopt the sleep techniques by knowing the appropriate occasion for transition between sleep and idle mode. Comprehensive computer based simulation results demonstrate that this integer proposal achieves better EE feature with negligible impact on quality of service (QoS) of user equipments (UEs).

Introduction
According to International Telecommunications Union (ITU), information communication industry (ICT) consume 10% energy world-wide nowadays while transmitted data volume still increasing approximately with a speed of 10 times every 5 years [1]. Because of the growing energy demands and costs caused by higher speed of transmission, in next generation communications, more attention should be paid on energy efficiency (EE). Thus, some organizations are calling for green cellular communications, for instance, 5GNow, EP-7, etc. [2]. And EE policy also attracts operators' attentions. For instance, orange claims that energy consumption of per user should be reduced by 20% comparing with 2006. In cellular section, it is estimated that nearly 57% power consumption coming from BS itself. Thanks to the author in [3], cellular zooming policy is adopted with sleeping mode for BS while transmission. Once the policy is proposed, great deal of attentions are attracted with plenty of further studies based on this. For instance, in [4], the cooperative transmission of downlink communications with BS and antenna switching off mechanism is proposed; in [5], the author further extends the study with radio frequency (RF) chain sleeping for multiuser MIMO system; in [6] and our prior work in [7], the antenna selection mechanisms based on cellular zooming with sleeping mode are studied for massive MIMO systems. But another significant issue comes from China Mobile [8] that ignored by all of those works, which acclaims that nearly30% power consumption of BS consuming comes from air condition and other facilitate equipments of the machine room. Therefore, more power can be saved by better architecture planning, such as the proposal of cloud radio access network (C-RAN) that raised by China Mobile. But to the best knowledge of us, even the C-RAN proposal itself is still some kind of theoretical concept rather than a realistic prototyping model for latest raise and fewer studies nowadays.
Things in core networks seem familiar, some sleep mode [9] and network coding [10] mechanisms also have been proposed towards energy efficiency. But most of them based on existing network model while seldom attention has been paid on the new technology with ICN, which is of better feature towards reliving the load, better security and EE and proved to be promising technology for future. And to our best knowledge, even less notice has been paid on the coexisting architecture of both although we should focus on with the integrity sight.
Another, although the sleep strategy has been widely studied both in core and cellular network. But while talking about the reality, how to sleep and when to sleep are still an obscure issue, say, as equipments typically associated with uninterrupted power supply (UPS) system, simply turn on or turn off UPS system cannot be a good choice especially within the massive MIMO circumstance; as frequent in-out performance of UEs in cellular, an mechanism is needed for transition between sleep and working mode of antennas; as electron devices (capacitor, inductor, etc.) have some specific working lives and can be significantly destroyed by current pulse during power on-off periods, protection method and trade off mechanism between energy saving of cellular system and working lives of electron devices are greatly needed.
Based on all of these, in this paper, we raise a new answer the question listed above. The contributions of our work are: 1. the integrate structure is proposed. 2. the power supply circuit of of core router and cellular BS is further ameliorated for the transition between sleep and working mode aforementioned. 3. based on these, a new mechanism is proposed for communications. The structure of this paper is organized as follows: Section II is proposed system model. The improved model is discussed in detail with C-RAN and ICN in this section. In section III, The EE of this proposal is analyzed for both wired and wireless section of this proposal. The performance of circuit is analyzed and the transition scenario is raised in section IV. And the integrate working mechanism is demonstrated here. In section V, the proposal is compared with sleeping technology that raised previously. In section VI, we conclude this paper.

Proposed System Model with C-RAN Architecture
In C-RAN, typically, it consists of a cloud core network connecting with virtual BS pool, a high-speed switcher, Gbs/s or even Tbs/s fibre link, and several BSs that connecting with the switcher. While comparing with traditional radio access network, the cooperative multi-point (CoMP) accessing procedure can be more easily accomplished within the BS cluster with advantage that all transmission and receiving procedures are processing by virtual BS pool. As power of the BSs in a BS cluster are supplied by a power system uniformly, more energy can be saved because the number of air conditioner and other equipments can be greatly reduced by this architecture as talked aforementioned. Within the massive MIMO background towards 5G wireless communications, in this paper, we separate the virtual BS pool into the plant and control center, switcher that connecting with cloud domain network. Which is shown as Fig. 1. The advantages are: the plant and control center provides power to each antenna of BS in the BS cluster; and it can decide to turn on or off the corresponding antenna with VLSI circuit and cable line that connecting with each of them.
Here the antenna selection and radio frequency (RF) chain shortage problem is left for further study. In this paper, we say that the selected antenna or antenna arrays with corresponding UEs are already the optimal one. And we assume that there are enough RF chain for transmission with perfect channel state information (CSI). Another, the author in [11] estimates that in core networks, for instance, with the rate of 100Mb/s, the terminal and switcher sections consume 30% and the fiber links are responsible for 4.5% of total energy consumption. Another conceive of this paper is, under the circumstances of C-RAN, it will be worthwhile and consequential to add the information centric network (ICN) [12] leverage into this architecture as a feasible element with the purpose of alleviating the load burden of core network, saving more energy. The purpose is achieved by efficient distribution of information platform with caches that equipped with each cluster. It can provide services like point to point (P2P) in dedicate networks. The conception of ICN mainly relays on named data objectives (NDO) that independent distributed and stored in diverse locations. With NDO, identify The different is: in traditional IP protocol based core network, core devices such as switcher and router, will deliver the request by IP address. In contrast, with the help of caches that equipped within each core cluster, request will be packed and routed by NDO information no matter where the information located. The benefit relays on the disruption tolerance characteristic it is. And it is a more safer model which different with trust verification between client and server that traditional IP protocol based on but provides security transmission with name integrity and origin verification based on NDOs. Similar with P2P technology, ICN possesses the mobility and multihoming of core devices especially on condition that end to end connections. In addition, with caches equipped with each distribution cluster, it can be an energy efficient technology comparing with IP protocol,this issue can be proved in the following part of this work.

Analysis of Energy Efficiency
Typically, EE is defined as bit rate divided by energy that consumed to maintain the rate, thus EE formula with bit/joule can be expressed as: where R, P denote for the bite rate and energy consumption that needed to feed the rate. While talking about EE optimization, we can have the formulas as: where P, P sum are the required energy for transmission, the total power of BS in cellular or working router in core network.

Energy Consumption of Wired section
In this proposed regime, wired optical fiber between BS and cluster added with optical fiber between different clusters in core networks consist of the wired transmission line. Typically, in wired networks, energy consumption mainly derive from the consumption of transmission signal, core routers (optical cross connect with abbreviation of OXC and transponders), inline optical fiber together with amplifiers established in. While working, the sleep mode is further adopted for OXC, transponder together with the optical fiber for energy efficiency as prior literatures. And we assume that the router is optimal throughout the cloud network. The power consumption model can be given by: where P o , P s , P c , n stand for the energy consumption of total wired section, transmission signal, core routers and the number of it. Under the assumption that optical fiber is divided by fiber section with Erbium Doped Fibre Amplifier (EDFA) and section without it and number of core router, denote α, L, L edf a , l for the number of EDFA that equipped within the optical fiber, distance from required information to the BS, per length that EDFA can back up, remained distance plus the distance from cluster to BS, P s can be evaluated by: where α can be indicated as: Typically the distance each EDFA can compensate is 80km. On condition that the gain of optical fiber without EDFA is G, attenuation of optical fiber β, thus the section of optical fiber that without compensated by the EDFA can be formulated by: where L BS stands for the distance from cluster to BS. That is because of the regime that by sleep mode of non-active OXC and transponder, from cluster to BS, there are same number of transponders and optical fibers for each active BS. In this case, P ase can be expressed by: where P n−1 t stands for the power that needed in the former hop, which is of the same power value at the last end of optical fiber equipped with EDFA on account of the hypothesis that EDFA can compensate all of the losses during transmitting. And n g stands for the transmission bit rate by Gbps. Assume that Cisco CRS-1 with 16 slots series is used as core router, which will consume about 10.9KW power for full-duplex mode, then while transmitting, P n−1 t can be expressed by P n−1 t = 10.9KW/16 for each working slot on condition that transition between sleep mode and working mode added. Finally, P t can be expressed by:

Energy Consumption of Wireless section
In Massive MIMO with cylinder disposition, typically, there is one massive MIMO BS with hundreds of antennas (here the number denotes as N) and K UEs where K << N (typically, for a massive MIMO system, there are 100∼200 antennas distributed by cylinder shape that serves 42 UEs.). For simplicity, we assume that the ergodic capacity can be formulated by perfect channel state information (CSI) through pilot of the transmitter.ZF-BF is used for interference eliminating where the ZF-BF matrix is W = [w 1 , w 2 , ..., w k ] . In ZF-BF, take W as the beamformer for w k channels within a N × K matrix, where w k a N × 1 vector. Here h k , channel from BS to UE is 1 × N complex-Gaussian entries vector with zero mean and unit variance. Then the total channel matrix, a K × N complex matrix, can be defined as Then the ZF-BF matrix can be expressed as: where (.) H and (.) −1 stand for conjugate and inverse transpose of a matrix separately. Then the received signal of the k th user can be expressed as: where x k denotes transmitted signal at BS terminal of the k th user, which can be expressed as x k = √ P k s k with P k , transmission power of UE k. And s k is transmission signal at BS terminal of k th user that obeys complex Gaussian distribution with zero mean and uniform variance, n k noise signal with zero mean and N 0 variance, γ the normalization factor of signal of the k th user with expression γ = W 2 F /K . And . 2 F denotes for matrix Frobenius norm. Suppose channels are constituted by components carriers (CCs) where CCs are sufficient for usage of kth users, and maximum bandwidth each CC can carry is B CCs . If SIN R k is taken as signal to interference plus noise ratio of the k th user. Received bit rate of k th UE can be expressed as: as the assumption, B CCS is known with a constant value, SIN R k should be focused. For simplicity, we assume that each UE associated with two antennas while transmitting, thus suppose the power of antenna array with number of n in each BS is P bs , while turn on the associate antennas with and turn of the unexpected antennas to sleep, for k th UE, the power is allocated by β = P bs 2K/n . Taking ρ = β/N 0 as the signal to noise (SNR), Thus, the SINR of UE k can be formulated as: According to equ.9, the beamforming vector can be deduced as: as described aforementioned, ZFBF can eliminate the intra-interference. Achievable ergodic rate of k th UE can be described as: According to random matrix theory, the matrix HH H is a central Wishart matrix. suppose the number of antenna is larger than UE, then the matrix would be a K × K central Wishart matrix with N degrees of freedom, and its covariance matrix I, a unitary matrix. Then tr{(HH H ) −1 } can be deduced as: as K, M(associate antennas) growing with a constant ratio α = M/K, it converges to a fixed deterministic value:E{ W 2 F } = K/(M − K), then max-sum ergodic of per cell can be formulated as: As estimated aforementioned by CMCC, the machine room is responsible for 28% energy consumption, which is ignored in most of prior literatures. Thus with energy consumption of machine room P mr added, the energy consumption can be described by: P sum = P BS + P mr (17)

Analysis of transition period
As surge pulse (thunder, pulse surge, etc) can also bring serious damage to electron devices, according to the circuit theory, typically, two bypass capacitances are used for eliminating the surge or high frequency current to protect the load, thus the antenna in this paper. The protection circuit model is shown in Fig.3.
Tanking the antenna as one capacitance devices with value of C3. On condition that it can be easily damaged by surge pulse current, which is similar to the performance of capacitance. The capacitance value of the whole circuit can be expressed with C = 1/C1 + 1/C2 + 1/C3. According to circuit theory, we can adopt RLC circuit model to describe the power supply system for transition between on-off period of antennas in cellular system. Which is shown as in Fig.  4.
On condition that power supply is turned off, according to the source free series RLC circuit model, while applying the KLV around the loop, we can have: By solving the equation ,we can have: where α = R 2L is the neper frequency and ω = 1 √ LC is the resonant frequency. Then the three types of solutions can be defined as: α > ω is the overdamped case, α = ω is the critically damped case, α < ω is the underdamped case. In electron devices, typically, the impedance of resistance can be taken as zero. And according to wsitching law u c (0 + ) = u c (0 − ) = U 0 , i(0 + ) = i(0 − ) = 0, the voltage of capacitance will be: While adopting the expressions i = C du dt , u l = L di dt , we can have the voltage of inductance by several operations: Similarly, while u c , U denotes for the capacitance voltage and power source voltage respectively, on the power on moment, applying the series RLC step response theory and after a series of operations, we can have: while ignoring the circuit resistance, u c (t) can be further changed to: then after adopting the functions i = C du dt , u l = L di dt , the voltage of inductance u l (t) can be expressed as:

Proposed Scheme towards on-off Transition
Typically, while analysing the second order oscillation circuit, the τ = 2π √ LC is used to denote the oscillation period with ω c = 2π/τ . Denote τ as working time of source free period, and one T denotes for one period of on-off transition. Then the voltage function of u c (t) can be combined as : U cos(2π( t τ )), source free period U (1 − cos(2π( t τ ))), step response period (25) similarly,the function of u l (t) can be expressed as: u l (t) = −U cos(2π( t τ )), source free period U cos(2π( t τ )), step response period It is known that the voltage supply is some kind of rectangle wave. It can be further took as two rectangle wave with different positive and negative values during the source free and step respond period. While talking about the protection of working life electronic device, typically, during one on-off period,the working voltage should not exceed the nominal voltage. In this case, the expression of protection of electron devices working life can be written as: by solving the problem, the expression of t 1 , t 2 should be further devided by Considering that the solutions of these two conditions are some kind of similarity, the solutions are given here with expression by: (28) or t 1 < τ arccos( 1 2 − cos(2π( t2 τ ))) 2π (29) The processing procedure can be accomplished with some pseudo-algorithm while communicating. And in this paper we adopt a simple antenna and RF chain selection mechanism with I a , I rf , S a , S rf , IN r , IN c , IN f stand for idle antenna, idle RF, state of the selected antenna and state of selected RF, required information by the request, information located in the cluster caches and information in far away clusters respectively. The antenna and RF chain numbers are assumed sufficient enough for each request. Perfect CSI feature is adopted simultaneously. For processing procedure, the pseudo-algorithm is described as in Algorithm 1.

Simulation Results
In this section, we evaluate our proposed scheme and compare with the stateof-art techniques in terms of EE performance. General simulation elements are shown in Table I. We evaluate the performance with EE feature of our proposed scheme while comparing with the latest EE proposal with sleep mode both in wired and wireless section.
Suppose the modulation mode is 16 QAM in wireless communications, one cluster associates with 20 BS, we can get the EE performance at wireless section as Fig. 5. It is obviously that the proposed regime with C-RAN possesses better EE performance because the energy consumption of machine is greatly deduced comparing with traditional architecture, where each BS equipped with one machine room. And in the curve of traditional sleep scheme, each sawtooth denotes for a machine room been added on condition that the number of UEs exceeding maximum UEs' number one BS can bear. And while the number of UEs growing, the EE curves trend to flat, that is because of the total maximum UEs' number restrict of one cluster.
In the wired section, we assume that the distance between two hop are 100km, in addition, the distance from BS to cluster is 10km that connected by optical fiber. The EE performance in terms of transponder and port sleep mode with ICN are shown as Fig. 6. We take the number of hops standing for the location of required information from UEs in BS coverage area. When required information located in ICN cache of the cluster servicing the BSs, that will be one hop, otherwise, number of hops added by location. In this   Fig. 6 manifests that more transponders are added for transmission because of the maximum 40Gbps one optical fiber can sustain. It is clearly that better EE performance can be obtain with ICN technology in wired core while serving as the data center of wireless communication as descibed in Fig. 6. That is because that with ICN, the UE can always get information with least routing distance. And with the help of sleep mode in unexpected router and optical fiber, great deal of energy can be saved.

Conclusion
In this paper, one modified ICT architecture is proposed based on the C-RAN and ICT technologies towards EE. Detail construction of the improved policy is described and the EE analysis is demonstrated. Based on all of these, the decision scenario of sleep scheme for EE is proposed with transition between sleep and working mode both in wireless and wired section. Sleep policy can be more realistic with the help of this proposed method. The performance of this regime is compared with simple sleep scenarios that proposed latest for EE. And it is shown that the policy of this paper possess better EE feature. This work can be further combined with imperfect CSI and antenna selection method for more reality circumstance.


Semantic Annotation of SOAP Web Services based on Word Sense Disambiguation Techniques

A key requirement for materializing the Semantic Web involves the annotation of resources and Web services with semantic metadata. This procedure is traditionally addressed as a manual task, which involves a high consumption of time and resources as well as the expertise on description formats and formal representations of knowledge, such as ontologies. Some research has promoted the development of mechanisms that partially automate the semantic annotation procedure, however, for the services particular case, those works lack of an analysis of the linguistic context of descriptor documents or interfaces, which provide adequate assignment of semantic annotations on the functional attributes of the services. In this context, this paper introduces a mechanism to automate the semantic annotation of SOAP services, supported by Word Sense Disambiguation techniques (WSD), from which it is possible to link the context of descriptor documents to the procedure of identification and association of ontological entities related to service attributes. This document discusses the mechanism described above, by developing an example, as well as the results of the experimental evaluation performed on a prototype that implements the proposal.

Introduction
The semantic annotations of currently available services on the web for easing processes of discovery and composition is an essential activity for the materialization of semantic web. Nowadays there is a huge amount of both resources and services available online, so that they have overwhelmed the search engines capability for effectively meeting the queries from users and software agents. The semantic web development and its introduction in service-oriented architectures, enables applications to be able to discover, invoke, compose and monitor in an automatic or semiautomatic, way web services that respond to particular needs. The information about the functionality of each web service is encoded in documents called service descriptors, which allow their discovery and retrieval (Cardoso, Miller & Emani, 2008).
The semantic annotation of web service descriptors allows automated agents to reason about the capabilities offered by the services and make decision regarding discovery and composition tasks, avoiding the complexity that those processes involve for users or developers. Thus, the amount of time required for service creation and deployment (time to market) tends to reduce, encouraging the creation of new value added services (Osman et al., 2006).
There are several research efforts regarding the semantic annotation process of service descriptors. However, while there exist approaches proposing mechanisms and tools for assisting the process of annotation (in a semi-automatic setting), this is commonly carried out on each of the attributes of the service in an isolated way, neglecting their meaning in the linguistic context configured by the rest of the elements comprising the service descriptor (Aksoy et al., 2011). Additionally, this semantic annotation process involves several task performed by hand by service designers and developers, turning it into a convoluted and error-prone process (Asswad et al., 2011).
The approach proposed by Chabeb et al., and documented in (Chabeb, 2009), conceives a SAWSDL extension called YASA4WSDL (Yet another Semantic Annotation for WSDL). According to the authors of this work, YASA4WSDL allows describing service attributes in terms of the concepts of two kinds of ontologies: Technical ontologies -comprising concepts describing functional and non-functional attributes of web services-and Domain ontologies -that incorporate concepts defining the business domain semantics, for example, tourism, health, etc. Nonetheless, in YASA4WSDL, both the association of ontological entities to the services attributes, and the selection of the ontologies used in the whole process, are procedures that have to be performed by hand by the users.
In contrast to Chabeb's et al. work in Bouchiha and Malki (2012) propose an approach for semi-automated annotation of WSDL descriptors. The authors of this paper conceive a tool for allowing the user to provide the WSDL descriptor to be annotated along with a set of ontologies. This tool executes two processes for deciding which of the provided ontologies delivers relevant semantics to the WSDL descriptor: (1) Categorization, in charge of classifying the WSDL descriptor according to its knowledge domain and defining which ontology is aligned with the specified domain -and (2) Comparison, which associates concepts of the chosen ontology to the elements of the WSDL descriptor. In this work and others related approaches like the METEOR-S platform (Verma et al., 2005) developed inside the LSDIS group (Large Scale Distributed Information Systems) of the University of Georgia, even though part of the annotation process is automated, the intervention of the user is still required for ensuring the appropriate annotation of the service descriptor. Additionally, the user must provide the ontology or sets of ontologies whose entities (concepts/ instances) then become the semantic annotations, by performing first a timeconsuming process of search.
In view of the above, this paper introduces a novel platform for supporting the semantic annotation process of Web and Telecommunication services (Telco) based on Word Sense Disambiguation (WSD) techniques, used for identifying the linguistic context of the service descriptors in order to enable the automatic association of ontological entities to functional attributes of the services. This paper also presents the results of the experimental evaluation performed on the platform we built, evidencing the feasibility of our approach for automating the semantic annotation of the web services and Telco.
The platform proposed in this paper supports the association of ontological entities available on the web to each of the services' functional attributes (service, portType and types), taking into account the meaning of those attributes within the linguistic context set by both the service descriptor and the ontology. In order to identify such contexts, we used WSD techniques enabling the proper assignment of senses to polysemic words according to the context they are in. Once the correct meanings are found, by measuring the semantic relatedness among them, it is possible to associate the ontological entities to the attributes of the service descriptor, thus fostering a high semantic correspondence between the content of the descriptor and the annotations attached to it.
In the next section, a description of the platform we built for automating the semantic annotation of SOAP services (Web and Telco) is presented. The third section deals with the results of the experimental evaluation we performed on the proposed platform. Finally, the last section addresses the main conclusions derived from the research we conducted.

Materials and Methods
Web and telecommunications services are important elements for developing component-based distributed systems and convergent services. For both the Web and Telecommunications domains, different architecture models promoting the deployment, publication and consumption of services have been defined: the world of web services has been largely dominated by the SOAP and WSDL standards; however, a resource-oriented paradigm known as REST has been widely adopted in recent years. On the other hand, in the telecommunications domain there is no standard language or framework for describing services. The most representative efforts in this regard are OneAPI and Parlay X, specifications based on Web standards and technologies which allow the formally description of telecommunication services capabilities through WSDL interfaces (Jie et al., 2009;Smith, 2009). Given these reasons and considering that version 2.0 of the WSDL standard supports the description of REST and SOAP services, we have decided to work with WSDL 1.1 and WSDL 2.0 service descriptors, this way comprehending the description of Web (SOAP and REST) and Telecommunications services. Next section introduces a brief description of the WSDL language.

Web Service Description Language (WSDL)
WSDL is a recommendation from the W3C's Web Services Description Working Group, and has become a standard for the definition of web service interfaces. WSDL enables to describe service interfaces regardless of the underlying technology supporting their operation. In June 2007, the W3C released the WSDL 2.0 (W3C, 2007) recommendation, in which it has been incorporated a number of improvements over the language, from its previous version WSDL 1.1. WSDL 1.1 describes a web service through six main components (Khalid et al., 2010): • types: describing the data types used in the interchange of messages when invoking a service operation. There are simple types (one data element) and complex types (a set of simple types). • messages: these are abstract representations of the transmitted information.
Typically, a message comprises one or more logical parts (parameters), for instance a message of a purchase order comprises the items being ordered, the price for each item, etc. These logical parts are defined through simple/ complex types. • portType: this component defines the combination and sequence of messages for each of the abstract operations (functions) hosted by the web service. Each operation description comprises an input message, an output message and optionally an error/fault message. • binding: this component specifies the communication protocol and data format of each operation and message defined within a portType element. • port: defines an endpoint by specifying a unique address for a binding.
• service: this element represents a composite operation, aggregating multiple related ports.

Automatic Annotator of Convergent Services (AA-CS) Platform
The AA-CS platform receives as input WSDL service descriptors (Christensen et al., 2001). The platform provides an application that allows the user to specify the URI of the service descriptor he/she intends to annotate; the platform retrieves the descriptor by issuing an HTTP request to the supplied URI, starting then the process of semantic annotation. Finally, the platform generates a file holding the annotations following the SAWSDL (Semantic Annotations for WSDL and XML Schema) standard format (Farrell et al., 2007). Figure 1 illustrates the components of the platform arranged in six modules. The subsections below deal with the description of each one of these six modules, by specifying their operation on a WSDL document belonging to a sample web service (Global Weather Service) presented in Figure 2.

Attribute Extraction Module
This module identifies the relevant terms of the service descriptor, namely those capturing the service functional attributes. The attribute extraction module is based on the process proposed by Falleri et al. (2010), which comprises the following phases: This module finally discards those pairs where type is Port, Message or Binding since the identifiers (attribute values) of these WSDL elements are usually the same used as identifiers for the remaining tags.

Tokenization:
Frequently the information provided in WDSL descriptors follows naming conventions adopted by programmers, e.g. using CamelCase compound words for identifying operations, types and services. Tokenization refers to the procedure that allows obtaining a set of tokens or terms composing a sequence of characters. This way, the information extracted by the type filter module is tokenized for generating a list of words comprising operation identifiers and data elements. So, for example when tokenizing the sequence of characters "MyGlobalWeatherService", this module obtains the following set of terms: {"My", "Global", "Weather", "Service"}. In the previous example, the procedure for splitting the composed term consists of detecting the alternate use of upper and lower case letters along the sequence of characters. This way, each pair {type, identifier} obtained by the previous sub-module is replaced by the pair {type, tokens}, where tokens represents the set of words (token 1 , token 2 , ..., token n ) comprising the identifier element. For the example above, the tokenization module obtains: {service, (my, global, weather, service)}.   (Ly, y otros, 2012). That is why this POS filter sub-module removes the pairs [token i , posicion i ] where posicion i ∉ (NN, JJ, VV, SYM).
For the example above this sub-module obtains the pair {service, ([global, NN], [weather, NN])}. So, in conclusion, the terms "global" and "weather" are the tokens specifying the functional semantics of the service attribute element.
Finally, the set of tokens the attribute extraction module obtains when applied over all the functional attributes of the global weather service, comprises the terms {"get", "weather", "cities", "country", "global"}.

Attributes Disambiguation Module
This module is responsible for finding the right sense for each token i gathered from the attribute extraction module, by applying WSD techniques. This procedure sets each token i as target token, estimating its sense in function of the remaining tokens, which are regarded as its context. Each target token has {S 1 , S 2 ,..., S mt } senses, and {C 1 , C 2 ,..., C n } tokens that make up the context. At the same time, each term of the C i context has {S i1 *, S i2 *,..., S in *} senses.
Using the WordNet::SenseRelate::AllWords (SR-AW) (Pedersen et al., 2009 and2005;Pedersen & Michelizzi, 2004) tool, this module measures the semantic similarity (R ki ) between each pair {S k , S in } being S k the k-th sense of the target token and S in the n-th sense of a token in the context window. Finally, all the R ki of each S k are aggregated and the sense with the highest value of R ki is assigned to the target token.
The process of disambiguation of each token i is conducted upon the descriptor's linguistic context. The subsections below describe in detail the whole disambiguation process.
1.2.2.1. WSD technique for service attributes: this technique performs the disambiguation of service attributes, supported on the WordNet lexical reference system (Miller, 1995). The SR-AW tool was originally developed for assigning the most appropriate sense to each word within a set of terms, according to the tokens they are surrounded by (context window). In the approach we introduce hereby, the set of words this tool receives as input corresponds to the tokens gathered by the attribute extraction module, which in turn become the linguistic context of the service descriptor upon which the disambiguation process is performed.
The disambiguation algorithm denotes the tokens in context window as: {t 1 , t 2 ,..., t n }, where t target (1 ≤ target ≤ n), is the target token (token being disambiguated). The platform sets the context window depending on the number of tokens gathered from each service descriptor. For example, a context window of size 5 comprises two words at the right of the target token, two words at the left of the target token, and the target token . Assuming that each token t i has m possible senses, denoted by {s i1 , s i2 ,..., s im }, the set of possible senses of a target token would be {s target1 , s target2 ,..., s targetm }. One of these senses is then selected as the most appropriate sense for the token t target .
Algorithm 1 formalizes the process of service attribute disambiguation. First, one of the input terms is defined as target token, while the remaining terms configure its context and the score of each sense of the target token is set to zero (score i =0). In order to obtain the most suitable sense for each of the attributes, the algorithm estimates the semantic relatedness between the sense s targeti of the target token, and each sense s jk . The algorithm computes the s targeti × s jk →  relationship for each t j token, where s targeti and s jk represent two senses of a pair of tokens in the context window. In this way, the measure of semantic relatedness receives two senses (s targeti and s jk ) as input and generates as output a real number that quantifies the semantic similarity between them. This relatedness value is assigned to temp-score k , and then the highest score is selected (best-score). Subsequently, the algorithm aggregates the best-score of each token in the context window, turning this into the score for the sense s targeti (score i ) of the target token. Finally, the sense s targeti holding the highest score is established as the right sense for the target token. : next if j = t 8: for each sense s jk of w j 9: temp-score k = relatedness (s targeti , s jk ) 10: best-score = max(temp-score) 11: score i = score i + best-score 12: return s targeti so that score i > score j for all S targetj in {s target1 , ..., s targetN } Considering the Global Weather sample service, let's suppose that it is required the disambiguation of the term "weather" (target token), given a context window consisting of {"country", "get", "cities" and "global"}. Thus, assuming that the weather token has five senses, the algorithm 1 computes the semantic relatedness between "weather" and each sense of the terms in the context. Thus, for instance, given the first sense of "weather" (S target1 ) being: S target1 : "the atmospheric conditions that comprise the state of the atmosphere in terms of temperature and wind and clouds and precipitation", And the first sense of the term "country" being: S 11 : "a particular geographical region of indefinite boundary (usually serving some special purpose or distinguished by its people or culture or geography)", the semantic relatedness between them is 0.585, such value is assigned to the temp-score k variable.
Assuming this similarity value being higher than the semantic relatedness scores obtained for the remaining pairs (S target1 , S 1k ), then the algorithm sets the best-score value to 0.585. Subsequently, the best-score is added for the S target1 sense of "weather" (score 1 ).
Lastly, assuming that score 1 is higher than the rest of score i values (the score associated to the four remaining senses of "weather"), the algorithm assigns S target1 as the most suitable sense for the target token "weather", according to the terms comprising its context window.
The outcome of this attribute disambiguation module consists of a set of tokens belonging to the service descriptor (e.g. "country", "get", "cities", "global" and "weather"); each one associated to its most suitable sense, according to the terms that compose its context window.
The time complexity of this disambiguation algorithm may be estimated by assuming S avg as the average number of senses for each of the terms registered in the WordNet dictionary. Thus, for estimating the similarity between each sense of the target token and each sense of the terms composing the context window, (S avg x S avg ) = S avg 2 comparisons are required. This way, for a context consisting of n terms, n x S avg 2 comparisons should be made. The algorithm performs this procedure for each one of the S avg senses of the target token and each of the tokens context window comprises. Therefore, the number of comparisons performed by the attribute disambiguation algorithm is about n 2 x S avg 3 . While S avg 3 affects the number of comparisons the disambiguation algorithm made, this factor is constant and independent from number of attributes being disambiguated. Consequently, it is possible to state that the execution time of algorithm 1 is proportional to the square of the context window size (n 2 ).

Ontology Search Module
This module integrates the Google Custom Search API (Google, 2012) enabling the AA-CS platform to use its search functionalities. By using this API, the platform is able to retrieve ontologies available on the Web whose classes, properties or instances match the terms (tokens) gathered by the attributes extraction module. In order to do this, a query is issued against the Google API (via HTTP GET), by specifying a service attribute value and setting a file extension restriction (using the filetype operator), so that the search results contain only OWL documents. The API returns a JSON document holding a collection of URLs pointing to the location of the matched ontologies.
Since this search engine classifies the most relevant results in the top places of the ranking (Lewandowski, 2010), the ontology search module takes only the first ten entries of the group of URLs the API retrieves. Figure 3 shows the collection of ontologies (URLs) matched for three tokens belonging to the Global Weather service descriptor (Figure 2).

Module of Ontological Entities Disambiguation
The previous module associates a collection of ontologies to each of the attributes extracted from the WSDL descriptor. The purpose of the ontological entities disambiguation is to identify which ontological entities (out of the set of classes, properties and instances each collection of ontologies comprises) may be used for annotating the functional service attributes. This disambiguation procedure starts by identifying the ontological entities matching the service attribute to be annotated (matching entities), along with their parent and children entities-according to the concept hierarchy in each ontology. Later, in order to estimate the semantic relatedness between the service attribute and the ontological entities, this module proceeds to disambiguate the matching entities, establishing their senses regarding the ontology they belong to.
An ontology reasoner supports the syntactic matching procedure (between service attributes and entities) allowing the search and extraction of ontological entities. There several tools that enable reasoning on top of ontologies. However, considering the use of OWL ontologies in the context of our proposal, tools like FACT++, HermiT, Pellet or RacerPro are suitable inference engines for serving this purpose (Dentler et al., 2011).
Particularly, we use the Pellet reasoner for performing the syntactic matching procedure, since (i) it natively supports OWL, including a subset of OWL Full, (ii) offers a wide support for XML Schema types, and (iii) Pellet is an opensource tool in a continuous development/improvement process (Parsia et al., 2005). Additionally, Pellet is the only framework offering a native interface for Jena, which eases the handling of OWL and RDF (Sirin et al., 2008) ontologies.
Next subsections detail the artifacts composing the module of ontological entities disambiguation: 1.2.4.1. Ontology Reasoner: allows identifying the ontological entities whose label (name) matches the attribute to annotate (token i ). It also gathers the parent and children entities for each one of the matching entities, which later becomes their linguistic context used for disambiguating them. Let's assume for instance, the token "weather", and the ontology located at http://www.scs.ryerson.ca/~bgajdero/ msc_thesis/code/ontologies/weather-ont-t2.owl. The ontology reasoner identifies a high semantic relatedness between the entity WeatherReport (matching entity) and the "weather" token. Likewise, the reasoner extracts the parent and children entities from WeatherReport: DatedWeatherEvent and TimedWeatherEvent.

Ontological entities disambiguation:
this sub-module disambiguates the matching entity associated to token i, according to the set of entities comprising its linguistic context, by using a WSD technique similar to the one introduced in the attribute disambiguation module (section 1.2.2).
This disambiguation procedure is performed by running an open-source tool called WordNet::SenseRelate::WordToSet (SR-WTS) (Michelizzi & Pedersen, 2008), which receives as an ontological entity as input (target entity) e t1 and a set of n entities {e 1 , e 2 ,…, e n } composing its linguistic context. The disambiguation procedure generates a pair entity-sense {e i ,s i } for each ontological entities being disambiguated. At the end of this procedure, a set of pairs {(e 1, s 1 ), (e 2 ,s 2 ), …, (e k ,s k )} is generated for each ontology.
In the example being discussed in the above section, the target entity (e t1 ) is WeatherReport while its context entities are DatedWeatherEvent and TimedWeath-erEvent. After running the disambiguation procedure, this sub-module obtains the following entity-sense pair: (WeatherReport, "Summary of the weather conditions. It often includes the forecast conditions for a specific area").

Module of Selection of Ontological Entities
This module receives a pair entity-sense {e i , s i }-obtained from the above module-and a pair token-sense {t ti ,s ti }-delivered by the attributes disambiguation module. It is in charge of identifying the most semantically similar ontological entity to each token i (disambiguated service attribute). In order to do so, this module estimates the similarity measure proposed by Wu & Palmer (WUP) (Wu & Palmer, 1994), between the input pairs: {t ti ,s ti } x {e i , s i } → , which quantifies their semantic relatedness (R). This measurement is made on each ontological entity matching the token i , while adding the R-value computed for entities belonging to the same ontology. This way, the module estimates the overall value of R for each matched ontology. The ontologies with the higher R-value are then submitted to a quality analysis for deciding which is the most suitable ontology for annotating the service attribute.
The module of selection of ontological entities comprises two components: 1.2.5.1. Filter of ontological entities: computes the semantic relatedness (R) between each of the service attributes (token i ) and their associated ontological entities. This sub-module dismisses those ontologies with lower overall value of R.

Quality
Analyzer: this sub-module performs an analysis for estimating the quality of the ontologies that the above filter delivers. Such analysis is supported on the Pellet reasoner (Sirin et al., 2008), which allows identifying misapplied OWL constructs and misspelled terms and typos within the ontologies. Additionally, Pellet allows building the taxonomic structure that arranges the domain concepts and terms, in a comprehensive and consistent way.
When detecting inconsistencies, Pellet locates the involved ontological entities at the bottom of the concept tree turning them into subclasses of owl:Nothing. Finally, Pellet calculates the number of well-formed concept in every single ontology, so that those ontologies with the lower number of concepts are discarded.

SAWSDL Generator Module
This module implements a procedure that generates a SAWSDL document (W3C, 2007), specifying the association between each attribute of the WSDL descriptor and ontological entities, by using the modelReference schema attribute.
We use SAWSDL as the language for encoding the semantic annotations, since it has been adopted as a W3C recommendation, defining a mechanism for attaching semantic information to WSDL elements. In the example below, the annotation attached to the attribute MyGlobalWeatherService (service label) is presented: <xsd:service name="MyGlobalWeatherService" sawsdl:modelReferen ce="http://www.scs.ryerson.ca/~bgajdero/msc_thesis/code/ontologies/weather-ont-t2.owl#WeatherReport"> </xsd:service> Where the label <xsd:service> denotes the WSDL element, and sawsdl:modelReference specifyies the ontological entity that annotates the service attribute.

Experimentation
In order to measure the relevance of the annotations generated by the AA-CS platform, we adopted the benchmarking methodology (Blakeman, 2002), using as gold standard a collection of manually annotated Web and Telco services descriptors (WSDL documents). This way, we contrasted the annotations from the gold standard against those generated by the AA-CS platform. The coming sections describe the service test collection, the performance measures we used and the results obtained from the experimental evaluation of our approach.

Performance Measures
For estimating the quality of the annotations associated to the service descriptors processed by the AA-CS platform, we adopted statistical measures widely used in characterizing the performance of information retrieval systems (e.g. search engines): Precision (p) (Ec. 1), recall (r) (Ec. 2), and F-measure (f) (Ec. 3) (Yatskevich, 2003). When contrasting the automatic annotations against those provided in the test collection three sets of annotations were identified: • True positives (VP) or correctly assigned annotations.
• False negative (FN) or semantic annotations that the AA-CS platform misses, despite their relevance.
Based on the cardinality of these sets the expressions below define the abovementioned quality measures: The precision measure estimates the reliability of the annotations made by the AA-CS platform, while recall specifies the ratio of correctly assigned annotations to the overall number of relevant annotations and F-measure determines the overall quality of the annotation (Yatskevich, 2003).
The above measures were estimated on each of the 94 descriptors comprising the test collection. For computing the general precision and recall of the whole annotation process, the macro-average (Lewis, 1992) method was used as follows: Where n is the overall number of matches made.

Evaluation Results
The experimental evaluation ran on a server with a 3.20 GHz dual-core processor, 8192 MB in RAM and Debian Squeeze as operating system. The analysis of the obtained results began by estimating the performance measures for each one of the 13 service domains defined in section 3.1. The estimation of these measures involves contrasting the annotations made by AA-CS against those annotation provided in the service test collection. Figure 4a illustrates the results for services descriptors belonging to Web services domains, while Figure 4g presents the performance measurements for Telco service domains.
The results depicted in Figure 4a, evidence a high precision value: 88% average over all domains. The traveling domain-comprising WSDL descriptors with fewer attributes (ranging between 2 and 14) in contrast to other domains-presents the best performance of the Web service domains: its corresponding F-measure is over 92%, which indicates that for this particular domain, the AA-CS platform properly assigns semantic annotations to the attributes of the services comprising it.
On the other hand, the less favorable performance of the platform corresponds to the communication, geography and health care domains. Services belonging to those domains feature a large amount of attributes (ranging from 57 to 66 attributes). Thus, it is possible to state that the performance of the implemented annotation procedure is reduced as the number of service attributes grows.
The results outlined in Figure 4b, regarding service belonging to Telco domains also evidence a high precision, being 94% average. The MMS domain features the best performance measures, which may be due to the low amount of attributes (6) extracted from the only service this domain contains, when compared with the quantity of attributes characterizing the rest of the services belonging to the other three domains (which range from 13 to 18 attributes). In this particular case, the F-measure reaches 88%, implying that the AA-CS platform properly associates semantic annotations to the attributes extracted from services belonging to the MMS domain. Furthermore, the SMS service domain presents the smallest value of precision (83%), while holding the highest number of attributes (18 in total) in contrast to the remaining domains. This confirms the abovementioned rationale stating that the quality of the annotation made by the AA-CS platform is inversely related to the amount of service attributes being annotated.
In general, the system's performance is substantially favorable for service descriptors with attributes ranging from 2 to 34, featuring an average F-measure between 87% and 92%. Processing descriptors with 35 to 66 attributes causes a slight reduction in the quality of the annotations associated by the platform exhibiting an average F-measure between 82% and 87%.
The slight drop in the overall platform performance when annotating WSDL descriptors with a large number of attributes is due to the rising error likelihood of the disambiguation mechanism when processing a wider context window. In such situation the context tends to include noise terms hindering the proper assignation of senses to the service attributes. This limitation may be addressed by setting a restriction on the number of attributes being disambiguated, i.e. when receiving a service descriptor featuring more than 34 attributes, the processing should be splitted and parallelized into various context windows. Source: authors' own presentation Figure 5 shows the relationship between the measures of precision and recall. This graph evidences the capability of the AA-CS platform for attaching highly relevant semantic annotations to each service attribute in the test collection, since all data points lie in an area of high precision (up to 95%) and recall (over 87%).

Conclusion
Existing approaches for supporting the semantic description of web service and resources require for the developers to mediate in the whole annotation process, which generally implies high resource consumption and a considerable effort from them. Most of those approaches even though associate ontological entities to service descriptors in a semi-automatic way, perform such annotation neglecting the meaning of the service attributes regarding the linguistic context they are in (i.e. the content of the service descriptor). Thus, the quality of the annotations in these kind of approaches could be low to the extent that they might not match the domain to which the services belong.
Unlike related approaches, the platform we introduced in this paper, is supported on WSD techniques for disambiguating both service attributes and ontological entities, allowing the semantic annotations to be consistent with the service domain. One major contribution of this work is the automated search of online available ontologies, promoting this way the reuse of a wide range of existing vocabularies specified under an agreed terminology by domain experts.
The results derived from the experimental evaluation performed on the AA-CS platform-in terms of precision, recall and F-measure-demonstrate that both the mechanisms implemented for disambiguating the functional attributes of Web and Telco services, and the technique for associating ontological entities to those attributes, are closely related to the judgment of humans when performing the same annotation process by hand. This evidences the benefits of using WSD techniques for articulating an automated mechanism capable of assigning highly related ontological entities to services attributes.
Further work in our research aims to adapting the proposed mechanism for assigning semantic annotations to web resources other than service descriptors, such as html documents and multimedia files.


Semantic Reasoning with Contextual Ontologies on Sensor Cloud Environment

This research first focused on processing enormous number of sensor events from a variety of city-wide sensor networks. As a solution, the well-known big data handling scheme, Hadoop cluster framework, drew, unquestionably, our attention. The acquired sensor events are to be used to immediately detect a certain abnormal situation within the framework. Accordingly, we integrated our existing context-aware collaboration framework with Hadoop cluster framework by interfacing data collection and context-aware reasoning parts of the existing framework with the Hadoop cluster framework. This approach enabled us to effectively process massive sensor events and semantically analyze the big data within the cluster environment. The proposed smart city sensor cloud framework provides ontology-enabled semantic reasoning scheme with the XOntology in combination with the Context-Aware Inference (CAI) model. By applying the ontology technology, the proposed framework enhances the availability and interoperability of the contextual information across many cooperating parties according to semantic reasoning results. Further, this framework is flexible enough to integrate any heterogeneous platforms including many existing IT solutions as well as mobile platforms. In addition, this approach presents the direction of progressive migration of many existing sensor network solutions into big data handling sensor cloud framework.

Introduction
Now sensors are everywhere around us. With the rapid progress in wireless telecommunication and multidevice sensor technology, wherever you go, you can expect any kinds of sensors waiting for you. They are to work to assist the traditional way of our lives by providing most of the existing IT solutions with their smart computing power and innovative services in ultimate smart city environment [1]. This results in producing explosively increased sensor data, meaning that we need to find a new data handling method to effectively process them and encourage various IT systems to collaborate with each other through increased information sharing for more accurate and pertinent services. On the other hand, it is true that we have to find an effective way to keep rapidly growing data volume to be ready for use at any place in real-time. As a solution, the well-known Hadoop [2] big data cloud framework is considered to support huge events data. The Hadoop based big data cloud framework, however, has latency limitation in processing the huge data due to its batch-oriented data processing mechanism [3]. In a sense, this makes it difficult to constantly monitor and immediately respond to a certain emergency situation or a specific attention required situation. Most importantly, the anticipated services for a smart city environment should deal with an urgent situation with agility as well as normal conveniences for citizens. This requires the framework to monitor the events and to detect abnormal situation among tons of incoming events.
As a solution, the existing ontology-enabled collaboration framework is combined with the Hadoop cloud framework to build a complementary cooperating system. The existing framework was first implemented as a scenario-based collaborative framework where numerous heterogeneous application services collaborate with each other according to the predefined service scenarios depending on specific event(s) from sensor network environments [4]. Particularly, rapid progress in wireless telecommunication and RFID/USN  sensor technology encouraged us to apply this advanced smart computing power to the existing IT solutions. The initial collaborative RFID/USN-enabled adaptive middleware framework has been extended to perform semantic analysis based on associated contextual data from numerous sources. For this, Context-Aware Inference (CAI) model with the contextual ontologies is applied to the framework as the common semantic analysis infrastructure [5][6][7]. Figure 1 describes the overall architecture of the smart city sensor cloud framework where a variety of sensorenabled services are supplied depending on contextual information resulting from the semantic reasoning within the integrated framework. The proposed framework includes Hadoop Distributed File System (HDFS) [2] as the main big data handling scheme of the sensor cloud infrastructure and the context-aware collaboration manager as the semantic analysis controller. The sensor cloud infrastructure contains a single master cloud node, sensor client nodes, and up to thousands of sensor data nodes. Especially, a collector agent is built on the sensor client node as a HDFS client and performs user-customized map and local reduce functions as a MapReduce [8,9] client. Incoming events streams are first saved on the real-time stream repository on the sensor client node and replicated on the pipelined sensor data nodes.
The collector agent in the front-end of each sensor client node not only writes the events into the sensor cloud as a HDFS client [2] but also frequently asks MapReduce functions to be executed on the newly accumulated events on the associated data nodes. In addition, the proposed framework takes full advantage of spatiotemporal locality with the customized MapReduce functions on the contextual events. Generally, semantic reasoning of the sensor events is requested by the context mapping agent to the semantic reasoning agent with the results of MapReduce functions which are applied to the events stored within the sensor cloud framework.
The context-aware collaboration manager mediates the flow of events from their acquisition at sensor client node to their consumption by application services through its semantic reasoning procedures. The context mapping agent first sorts out valid contextual information among numerous incoming events with global reduce functions and determines whether the reduced results need to be semantically observed by the semantic reasoning agent.
The previous ontology-enabled context-aware collaboration framework includes semantic reasoning scheme over continuously incoming contextual events streams that can trigger application services with proper information. Basically, the application services collaborate with each other by sharing contextual information across many involved parties. In order to provide end-users in a smart city environment with the highly sophisticated services of interconnected International Journal of Distributed Sensor Networks 3 applications, we need to consider an orchestrating service framework performing comprehensive analysis about the acquired contextual information. This requirement results in integrating ontology-enabled semantic reasoning scheme within the context-aware collaboration manager which adopts many features from the initial scenario-based collaborative framework [4][5][6][7].
Furthermore, smartphones allow the users to stay connected to the Internet at all times, and the pervasiveness of the sensor devices gives them the opportunity to use the information from the devices. These factors increase the need to process a huge number of data transactions and infer the present situation from the current context. The factors that are included in the current context contain the owner's current location and time, status values of various sensors, media preferences, community accessibility, and so on. This new environment leads to the development of personalized context-aware services, too.
With the introduction of the semantic reasoning agent, the applications work more efficiently and comprehensively than before because each service is defined by much finer context-driven scenarios. For example, when a handicapped passenger with a RFID-embedded boarding pass is approaching a boarding gate, the airline employee is already aware of his/her arrival and is going to be ready to service required assistance for the passenger. The semantic reasoning agent has been evolved by strengthening ontological reasoning features in the context-aware collaboration framework. In addition, the semantic reasoning agent includes the ontology definitions which define the entities of real-world objects and their properties in order for the application services to share the concepts and relationship information. The adoption of ontology into the framework results in enhancing the interoperability and system reusability. The semantic reasoning agent includes the XOntology as a context representation scheme which is shared by related application services. The integration of an ontology scheme not only allows applications to share contextual information based on the common ontology specifications but also increases the collaboration rate between application services. The interoperable XOntology is composed of core and extended ontology sets. The core ontology includes person, place, and time information. The extended ontology covers high-level applied concepts such as inventory management, logistics, surveillance function, and environmental monitoring.
This research includes the CAI model [5][6][7] specialized to the ontology-enabled context sharing across the contextaware collaboration framework. This option is made on the basis of the general situation analysis in combination with the ontology specification manipulating the real-world entities and their relationships.
This paper is composed of 5 Sections in total. Sections 1 and 2 present the introduction and the related research. Section 3 describes the sensor events handling and semantic reasoning schemes on the sensor cloud framework. In addition, the XOntology in terms of smart airport environment is discussed. In Section 4, the CAI model is described based on contextual situation analysis. Section 5 concludes with future research direction.

Related Research
The demands for handling rapidly increasing contextual events stream and their interoperability through context sharing leads us to study adopting the following areas: big data Hadoop framework; context-awareness; interapplication context sharing; and context manipulation with ontologies.

Hadoop Big Data Framework.
As the well-known big data framework, Apache Hadoop software library allows for the distributed processing of large data sets across clusters of computers [3]. The framework is mainly composed of Hadoop Distributed File System (HDFS) for providing high-throughput access to application data and MapReduce scheme for parallel processing of large data sets. On the other hand, Hadoop YARN or MapReduce 2.0 works as a framework for job scheduling and cluster resource management. The two major functionalities of the JobTracker, resource management and job scheduling/monitoring, are handled by separate daemons, a global ResouceManager (RM) and per-application ApplicationMaster (AM). In this architecture, the ResourceManager controls resources for all the applications in the system by allowing per-node slave, the NodeManager (NM), to form the data-computation framework, whereas the per-application ApplicationMaster works with the NodeManager(s) to execute and monitor tasks. The ResourceManager has two main components: Scheduler and ApplicationsManager.

HDFS.
HDFS is a distributed file system and a framework for the analysis and transformation of very large data sets using the MapReduce paradigm [8]. Typical HDFS is composed of a single NameNode for each cluster and hundreds (and even thousands) of DataNodes. The NameNode holds file system metadata in memory and keeps track of the namespace tree and the mapping of file blocks to DataNodes. Each DataNode performs a number of Hadoop application tasks on its data. User applications access the file system using the HDFS client library such as read, write, and delete functions.

MapReduce.
MapReduce is a programming model and an associated implementation which are automatically parallelized and executed on large clusters of commodity machines. MapReduce offers easy to use programming scheme, even for programmers without experience with parallel and distributed systems, since it hides the details of parallelization, fault tolerance, locality optimization, and load balancing that are entirely transparent to the programmers [9,10].
Hadoop divides each MapReduce job into a set of tasks [11]. Each chunk of input is first processed by a map task and converted to the sequence of ⟨key, value⟩ combinations as the result of customized map functions. After completing individual nodes' map function, all the results are reviewed and merged upon key values with the user-provided reduced function [2].

Context-Awareness.
In the earlier stage of context-aware technology, many people wanted to define their own meaning of "context, " and many types of variation of "context" were found. The term, "context-aware, " was introduced in Schilit and Theimer (1994) [12] for the first time. The authors describe context as location, identities of nearby people and objects, and changes to those objects. Three years after, Ryan et al. (1997) [13] referred to context as the user's location, environment, identity, and time. After that, one of the most accurate definitions is given by Dey and Abowd (2006) [14], who regard context as "any information that can be used to characterize the situation of entities (i.e., whether a person, place, or object) that are considered relevant to the interaction between a user and an application, including the user and the application themselves" [15].
As various kinds of event data and services interact with each other in an environment, a single event can relate to numerous services, and several different events may have to do with the same specific context. This means that the framework can link interconnected environments between physical devices and application services. Also, we realized that original events and the identified context data which exist within the framework should be mutually understandable by related parties. As a result, an ontology scheme is additionally applied as mutual communication method in our previous context-aware collaboration framework. This method not only allows applications to share contextual information based on the common ontology specifications but also provides a powerful way to develop adaptively collaborative services.

Interapplication Context Sharing.
Since an Internet connection is available at any time and any place, we have faced an increasing demand to take full advantage of this highly intelligent environment where multiple data sources are available. Particularly, the demand leads us to develop a ubiquitous infrastructure framework in which all the context data collected from various sources is managed and shared across the correlated applications of all interested parties.
First of all, the underlying framework needs to understand various kinds of context information according to a common context description. This is done in order to enhance the effectiveness in representing and sharing the information. Most importantly, the common context description scheme needs to be clear and general enough to be accepted by all related parties.
As a solution to representing contextual information, RDF (Resource Description Framework) [16][17][18][19] and OWL Web Ontology Language [16,[20][21][22][23] of the Semantic Web standards are applied to the common context description scheme of the framework. According to the World Wide Web Consortium (W3C), "the Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries. " The ultimate vision of the Semantic Web is to make automated semantic agents access the Web on their own and carry out tasks only by referring to the semantics encoded into the Web page on behalf of users [24].
In the Semantic Web, we regard the things in the world as resources: a resource can be anything that someone might want to talk about and that can be easily understood as a thing or an entity [25]. In current environment, a user's current location, real-time traffic information, environmental information like temperature, bioinformation like one's blood pressure and glucose level, and a RFID tag attached to a traveler's luggage are all examples of things to be focused on.

Context Manipulation with
Ontologies. The current ubiquitous computing environment contains lots of smart objects working with their corresponding application services. This phenomenon requires us to recognize the current situation out of the complicated combination of contextual information. In addition, the application services need commonly understandable scheme to share the contextual information in this environment. First of all, the sharing of current context begins with the clear definition of common vocabularies. Once we build the dictionary of the common vocabularies, we further proceed to detect a situation determined by the individual or combined contextual information. This process is treated by adopting an ontology scheme as a tool for denoting the contextual information and describing the relationships among the individual entities.

2.4.1.
Ontologies. Ontology has been described as "a specification of a conceptualization, " by Gruber in 1995 [21]. He mentions that "what "exists" is exactly that which can be represented. " In other words, all the intelligent equipment and objects in the current computing environment should be represented in a certain ontology language and shared by various parties. In the real world, especially Semantic Web world, ontology is about "the exact description of things and their relationships" [26].
A general ontology model describes concepts of target domain, properties and attributes of these concepts, and constraints on properties and attributes. Additionally, it optionally examines individuals, defining a common vocabulary and encouraging a shared understanding [27].
On the other hand, Neches et al. emphasize the importance of ontology as a method to enhance knowledge sharing and reusability across different applications [28,29]. Once ontologies for a specific domain have been built, it can be shared and reused for other domains by adding new concepts into the existing one.
As a domain modeling tool, ontology languages such as RDF (Resource Description Framework), RDFS (RDF Schema), and OWL Web Ontology Language of the Semantic Web standards describe the common vocabularies and their relationships within the framework.
RDF is a primary representation language for domain models or ontologies. It allows us to define things, namely, resources as well as their properties, the relationships between resources [14,17,30]. RDF is composed of triples (statement), subject-predicate-object, and the formation of a graph of the resources [31]. RDF serves as a generalpurpose language for representing information in the Web, whereas RDFS includes the vocabulary of RDF [19]. RDFS is introduced to strengthen the semantic capability of RDF. In other words, it provides a syntactic specification mechanism to allow us to describe resources as classes and their relationships as properties.
In OWL, a class denotes a group of individuals characterized by a certain common attribute, and it contains the set of objects. While modeling a domain of interest, interclass relationships are described as a set of subsumption (super class-subclass hierarchy) relations, a collection of superclass-subclass relationships [32]. These relationships are represented as a class hierarchy. In a similar way, a hierarchy of properties, attributes, or relationships is formed to a property hierarchy in OWL scheme [20,23].
In Section 3, the XOntology of a smart airport prototype environment is introduced with the semantic representation of the domain by using the Protégé [33] ontology development tool.

Reasoning Out of Formal Ontologies.
Knowledge is represented in the form of ontologies since machines interpret and express concepts, relations, and specifications. The knowledge base is extended by deriving new facts from existing ontologies. Knowledge can be formalized by using knowledge interchange format (KIF) [21,34], conceptual graphs [35,36], or description logics (DL) [17,37,38]. Among them, description logic is selected to formally represent concepts because it is expressive enough to describe logics of realistic applications. Usually, a reasoner checks consistency of the T-Box of terminology which describes terminology and rules and the A-Box of assertions, which looks at assertions about individual concepts. It also checks subsumption relationship across the existing relations [37][38][39][40]. This scheme is integrated into the existing collaboration framework as an ontology gateway, the semantic reasoning agent, which can be used as the ontology-enabled context-aware knowledge option.

Semantic Queries Using SPARQL.
In this paper, context sharing across collaboration framework is supported by defining context ontologies as XOntology and reasoning contextual information by querying the RDF triples of the XOntology through SPARQL (SPARQL Protocol and RDF Query Language) API [41,42]. This approach is expected to increase the interoperability and availability of the contextual information which encompasses tag, location, USN sensor, behavioral, and alert information of the constituent individuals of the XOntology. Semantic queries are supposed to find basic relationship among a variety of instances of the classes and derive logical anomalies from the current context according to the semantic rules of the ontology across collaboration framework. Depending on the query result, the framework triggers related services and enables individual service providers to provide ontology-enabled context-aware services.

Sensor Cloud Framework
Generally, a typical sensor event contains its spatial reference that could help find the place of its occurrence with the best available positioning method and its timestamp. Even though each event is stored into the separate sensor data node based on HDFS data replication policy, a block as a unit of a data chunk naturally contains sensor events from physically close sensors within narrow time period. This leads us to focus on effective use of the sensor cloud framework by fully making use of its spatiotemporal locality and conferring the benefit to many sensor-enabled services throughout the whole framework.

Sensor Events Handling on Cloud.
In the proposed sensor cloud framework, any events from sensors are first acquired by corresponding sensor client nodes (SCNs) and replicated on many sensor data nodes (SDNs) without further intervention from the master cloud node (MCN) once the distributed file system set-up has been established. The MCN exists per cluster and engages in maintaining the mapping of the sensor clients and multiple SDNs (see Figure 1).
Normally, the sensor cloud framework requests the individual SDNs to execute map functions over the events on them from diverse perspectives such as their location, sensor types, or tag identification numbers. The initially classified events by map functions are then locally reduced according to the major categorization factor such as timestamp, sensor values, or regions as the optimization method. Then the intermediate sets of combination of key and partially reduced set of values are aggregated by the global reduce function by the context-aware collaboration manager depending on sensor applications.
In this research, the existing collaboration framework is combined with the Hadoop Framework [3] to make the framework scale and fault-tolerant across the numerous nodes as well as make use of concurrently executed MapReduce functions on distributed sensor data nodes. Before we further proceed to explain main functionalities of the sensor cloud framework and the associated applications, the categories of the sensor events for smart city environment need to be examined: RFID or any identifiable tags; sensor device events; and social message events. Each event generally contains its id, the best available spatial reference information, and the correspondent timestamp. The accuracy level of the spatial reference information varies from GPS-level to Wireless base station-level or to RFID Antenna-level quality depending on the type of sensor devices and the current environment.
MapReduce functions can be customized to reduce the latency and maximize the parallelism at a massive scale large cluster environment. As a solution to this requirement, spatiotemporal data processing approaches are combined with the typical way of alpha-numeric data handling in Hadoop based distributed data processing framework. Particularly, the nature of spatiotemporal locality at the same cluster could enrich the performance of the framework. First the map functions can be classified into MapByLocation, MapBySensor, and MapByTag depending on the nature of the events and target services. The subsequent reduce functions include ReduceByTimestamp, ReduceBy-Context, ReduceByRegion, ReduceByValue, and Reduce-ByTopology. Among them, ReduceByContext adopts the ontology-enabled semantic reasoning scheme with predefined ontologies combined with the Context-Aware Inference (CAI) model of the existing collaboration framework. Based  on the properties of the ontologies and behavioral activities of individual event instances, the ReduceByContext function semantically evaluates the current contextual information and responds on any unexpected situations according to the semantic reasoning results.
The following two figures introduce the sequence between class entities across the whole sensor cloud framework. Figure 2 describes how the sensor clients save the acquired sensor event on DataNodes of the sensor cloud infrastructure (HDFS). Figure 3 shows how the MapReduce clients interact with the context-aware collaboration manager and the sensor cloud framework. The MapReduce function requested by the context-aware collaboration manager will be run on DataNode(s) as individual tasks and globally reduced to produce the finally aggregated result.

Semantic Reasoning.
For semantic reasoning of sensor events, the context mapping agent determines whether an event (fact) is contextual information associated with the predefined properties of the XOntology. The semantic reasoning agent, then, queries RDF triples stored in the ontology repository by using Jena SPARQL API [42] and query engine. It also evaluates current context as either normal or abnormal state after reviewing if it violates the predefined situation analysis (SA) rule of the CAI model. Figure 4 depicts the component and event flow of the semantic reasoning process of the context-aware collaboration manager. This allows the ontology-based applications to build and share the XOntology model with common vocabularies and flexible contextual reasoning within the framework.

XOntology.
The context representation of the framework is described by the XOntology, which is composed of the core and extension ontologies. By defining the common ontologies, identified context is understood and shared between the related parties of the framework. In addition to defining the common concepts as XOntology classes, the relationships between the ontology classes are also defined as properties (see Figure 5).
The core ontology classes encompass person, place, and time concepts, and the extension ontology describes highlevel concepts that depend on domain specific concepts or entities. The example of ontology describes the ontology hierarchy of an application domain of the smart airport mobile security control system, which includes agent, aircraft, cargo, event, IT systems, mobile device, public transportation, security device, service, and vehicle. The individual classes are not only drawn by analyzing real-world airport management rules, regulations [43], and the latest airport IT solutions [44], but they are also derived by combining general security issues in the computer security handbook of NIST (National Institute of Standards and Technology) [45].
The XOntology is defined by using the Protégé ontology development tool, where the ontologies written in the Protégé are exported into a variety of formats including the RDF(s), the OWL, and the XML Schema [30,33,46].

Ontology Classes.
The constituent subclasses of the core and extension ontology classes in the XOntology scheme are described in Tables 1 and 2 [30,33,46].

Ontology Properties.
Relationships between classes and attributes of a member of a class form property hierarchy in the XOntology scheme. The properties are classified into the object properties and the data properties (see Table 3).

XOntology Modeling with
Protégé. This section explains the XOntology modeling specifications designed on Protégé.    Once the XOntology has been developed, it can be shared and reused by many applications. Before we proceed to develop the ontology hierarchy of a domain, we need to list the key terms (entities) and their properties as in the previous two tables. After then, the derived terms are defined as classes of the domain and relationships as properties in Figure 6. The instances of classes are defined as individuals associated with the properties of the XOntology (see Figure 7). Individuals are introduced within the reasoning framework as instances of individual classes. They are interrelated with the corresponding properties in the form of RDF triples. OWL as an ontology language has three types of properties: object properties, data properties, and annotation properties [30]. The object properties explain the relationships between two resources (individuals of classes), whereas the data properties relate a certain data value to an individual. The annotation properties are used to add metadata to classes, properties, and individuals.

Knowledge
Reasoning with SPARQL Queries. SPARQL is designed to query RDF [47]. The query language structure is similar to SQL query processing scheme in that it allows the query processor to search for data that meet the conditions. SPARQL queries are used to determine whether a certain activity or behavior is supposed to occur within the ontology-enabled context-aware knowledge framework. SPARQL query lists the latest occurrence of individuals from the RDF triples in Figure 8. In this example, "Yanggon Kim" is not supposed to be found at the security ID area. By combining this result and situation analysis rule, the framework performs the contextual reasoning and shares the result with other correspondent parties.
By allowing SPARQL queries to find logical anomalies against the predefined properties, we can keep the whole framework in place. Further analysis based on the CAI model is discussed in the following section.

CAI-Based Situation Analysis (SA)
The CAI model is supported by the object properties and the data properties which are used as assumptions and constraints for the fundamental situation analysis and categorized in the form of the SA rule from the development of the previous context-aware collaboration framework [5][6][7]. The SA rule is composed of the predefined behavioral norms including existence availability (EA); the access availability International Journal of Distributed Sensor Networks     (AA); the expected transit time (ETT); and the sensor range (SR). All the facts like real-time contextual information such as tag or location information, timestamp, USN sensors data, and any other behavioral activities are reviewed to decide if they are eligible for the semantic guidelines of the SA rule.

Existence Availability (EA)
Norm. The expected pairs of places and their associated availability of simultaneous existence are used for validating identification results and figuring out the abnormal identification. Based on the EA norm, the ontology context reasoned is to decide whether a person could appear at different places at the same time.
Algorithm 1 shows that a person ("Kyungeun Park") is identified at different places at the same time and indicates that she may cause a certain security violation.

Access Availability (AA) Norm.
The AA norm is used for validating whether any person is passing or staying in unapproved area, scanning all recognized person objects in working memory for any possibility of illegal pass. The AA norm validates whether identified person or employees at a certain place of an airport are eligible to be there with a proper ID card or boarding pass.
Algorithm 2 shows several access violations resulted from illegal access with invalid ID. For example, in order to access ETT Violation::Amanda Maryland: 75(min) spent between @ Lounge and @ Gate020 "AirOperationsArea, " the "A-" or "B-"type IDs are required and a person with departure boarding pass should not be recognized in "ImmigrationCheck. " In addition, departure boarding passes or employee IDs are required to access boarding processing places like "SecurityCheck" or "Pass-portControl. "

Expected Transit Time (ETT)
Norm. The expected transit time rule is used to determine the earliest (MinTime) or the longest (MaxTime) expected time of arrival to move from one location to another. Each combination of two different places, where RFID reader or any other identification facilities are installed, is assigned MinTime and MaxTime, presenting time constraints. This information is used to deduce an abnormal behavior of individual passengers or employees in an airport by comparing time difference between the expected transit time and the actual time spent to move. Algorithm 3 tells "Amanda Maryland" spent 75 minutes to move from "Lounge" to "Gate020, " which takes 15 minutes longer than the maximum transit time, registered with 60 minutes. This matching result will alarm the system administrator and make him (her) keep track of the person and take security measures, if needed.

Sensor Range (SR) Norm.
The allowable ranges of sensors are provided in the XOntology as data properties. An abnormal sensor value is to be identified by the analytic matching procedure of the semantic reasoning agent and the associated action is performed.
In Algorithm 4, abnormal cases are listed after matching the SR norm.

Conclusion and Future Work
This research was motivated by matured sensor-enabled smart city environment. Regarding this, we first noticed the enormous amount of sensor events from a variety of citywide sensor networks. As an effective scheme to handle huge amounts of sensor events, the well-known Hadoop cluster framework was first considered. Mainly, the framework should continuously monitor the whole incoming sensor events and immediately detect a certain abnormal situation from them. Furthermore, the framework needs to work as an integrated collaboration infrastructure for various sensor-enabled application services. As the solution to this, we extended our existing context-aware collaboration framework to be seamlessly integrated with Hadoop cluster framework. This approach encouraged us to efficiently process massive sensor events and semantically analyze the big data within cluster environment. The proposed smart city sensor cloud framework includes Hadoop Distributed File System (HDFS) as the main big data handling scheme of the sensor cloud infrastructure and the context-aware collaboration manager as the semantic analysis infrastructure. The customized MapReduce functions are introduced on top of Hadoop cluster framework to take full advantage of the spatio-temporal locality. The ontology-enabled semantic reasoning scheme with the XOntology is combined with the Context-Aware Inference (CAI) model for various smart city sensor applications. By applying the ontology technology, the proposed framework enhances the availability and interoperability of the contextual information across many cooperating parties according to semantic reasoning results. This framework is flexible enough to include any heterogeneous platforms including many existing IT solutions as well as mobile platforms. Also, this approach is expected to be applied to many existing frameworks where massive contextual data from numerous sensor devices need to be handled in the future smart city environment. As a future work, big data clustering and optimizing schemes and the associated analysis/mining technologies need to be combined with the proposed framework.