Alti Adel

Boukerram Abdallah

Roose Philippe

Selector: A tool for dynamic service selection and management

Keywords: aware, selection of services, quality of service, context matching

This paper presents the tool SELETOR which has been developed for services selection. This paper proposes also a new web management system that allows for considering quality and context-awareness while discovering and selecting web service. The main idea of the proposed web service oriented architecture consists in the ontology Context-aware Quality Semantic Web Service called (CxQWS). At the first step, services are defined as a set of semantic metadata, reflecting service requirements and QoS parameters. At the second step, services with a semantic contextual metadata are elaborated. Such a procedure ensures that the selection decisions should be based on the semantic quality representation of the created services. The tourism services in a mobile environment have a critical role in creating tourist satisfaction. They are neither a uniform group, nor able to give consistently high service quality. Indeed they have significantly different platforms and a variety of heterogeneous service providers which make the management of service qualities complex. This complexity is clearly noticeable when a given service is provided by two or more providers, within the same area of the mobile clients; a decision should be made to select the most appropriate service based on contextual description with the best QoS.

INTRODUCTION

ECENTLY the World Tourism Organization emphasized that the future of the tourism industry depends on the ability to instill a culture of mobile communication and quality in tourism services [START_REF] Pashtan | CATIS: A Context-Aware Tourist Information System[END_REF][START_REF]Quality in Tourism, A Conceptul Framework[END_REF]. The tourism services in a mobile environment have a critical role in creating tourist satisfaction; they are neither a uniform group, nor able to give consistently high service quality. Indeed they have significantly different networks environments and a variety of heterogeneous service providers which make the management of service qualities complex. This complexity is clearly noticeable when a given service is provided by two or more providers, within the same area of the mobile clients; a decision should be made to select the most appropriate service based on contextual description with the best quality of service. The identical services have common semantics characteristics (name, city, category), within different providers, versions, QoS and executions contexts.

The heterogeneity of components regarding embedded sensors, CPU power, communication mechanisms (GPRS, WIFI, Bluetooth, ZigBee, etc.), speed of transmission as well as the media variety (sound, video, text and image) requires taking into account adaptation to a semantic level in order to avoid the ad hoc solutions which are not reusable and/or generalized. Diversity of languages, protocols, and hardware platforms lead to major incompatibility issues. Moreover, multimedia services adaption guided by user and community requirements and preferences are not easy to be performed in this context. Communication protocols, security, and QoS are other non-functional concerns which present new design challenges.

The application of context-awareness has been demonstrated in a large number of tourism applications [START_REF] Abowd | Cyberguide: a Mobile Context-Aware Tour Guide[END_REF][START_REF] Pashtan | Personnel Service Areas For Mobile Web Applications[END_REF]. Context aware computing plays an important role in tourism applications to better understand user context and adapt service with the continuous evolution of its context. The web service is an ideal fit in the tourism services, because it allows the service easy integration over heterogeneous systems.

The semantic web service ontology (OWL-S) offer sophisticated capabilities including automated discovery, composition, invocation and monitoring [START_REF] Mcllraith | Bringing semantics to web services[END_REF]. But OWL-S ontology does not address how to consider nonfunctional concerns, i.e. how to represent them. Furthermore, OWL-S doesn't really take into account the contextual information in web services quality selection for very limited mobile device like PDA, Smartphone, etc.

In this paper, we present the tool SELECTOR which has been developed for service selection. We present also the ConteXt-aware Quality of Semantic Web Service Ontology (CxQWS) includes support for web service quality control and resources requirements changes. The explicit separation of functional and non-functional concerns is the main concern of the CxOWS aiming to well capturing resources-awareness and controlling quality at semantic level.

The contribution of this work is deals with a semantic level which help improving services qualities provided to tourists in a discovery and interaction mobile environment. The goal of the proposed ontology is to provide semantic level facilities that allow the management of quality parameters of heterogeneous services. It provides also a dynamic adaptation of selection services based on context metadata parameters (user, environment, device, The key idea of the proposed web service oriented architecture consists in the Ontology ConteXt-aware Quality of Semantic Web Service (CxQWS). At the first step, services are defined as set of semantic metadata, which reflect service requirements and QoS parameters. At the second step, services with a semantic contextual metadata are elaborated. Such a procedure ensures that the selection decisions should be based on the semantic quality representation of the created services.

----------------  Adel Alti
The rest of this paper is organized as follows: Section 2 presents our motivations and other related works. Section 3 details CxQWS ontology, while section 4 presents an overview of the proposed framework. Section 5 presents application scenarios of our ontology as well as implementation issues. Finally, Section 6 concludes and presents some perspectives.

MOTIVATIONS AND RELATED WORKS

Management of Context-aware Web Services Qualities

There is a diversity of contextual information and a variety of quality attributes, we find several ontological languages such as WSMO [START_REF] Wsmo | Web Service Modeling Ontology[END_REF], DAML-S [START_REF] Horrocks | CDAML+OIL : a Description Logic for the Semantic Web[END_REF], ConteXtML [START_REF] Rayn | ConteXtML : Exchanging Contextual Information between a Mobile Client and the FieldNote Server[END_REF], CxBR [START_REF] Gonzlaez | Context-based representation of intelligent behavior in training simulations[END_REF], CWSC4EC [START_REF] Boukadi | CWSC4EC : How to Employ Context, Webn Service, and Community in Entreprise Collaboration[END_REF], CA-Discovery [START_REF] Suraci | Context-awareness in Content Discovery architectures[END_REF] and CxG [START_REF] Brezillon | Context-based modeling of operators Practices bu Contextual Graphs[END_REF]. These languages provide the means for defining QoS and context in specific application domains such as pervasive and mobile computing. However, neither various communication protocols, not heterogeneous services are discussed. Our approach addresses the limitations of existing approaches, suggesting the mechanisms to integrate new tourism quality attributes of mobile-based application, to provide a dynamic adaptation of selection services based on context metadata parameters (user, environment, device, and service provider context) and the management of the heterogeneity of service needs, of mobile devices capacities and their various communication protocols (GPRS, WIFI, Bluetooth, etc.) as well as the media variety (sound, video, text and image).

A semantic-based architecture for intelligent destination management system is proposed in [START_REF] Kanellopoulus | A semantic-based Architecture for intelligent Destination Management Systems[END_REF]. In this architecture, a semantic web ontology is used to model the tourism destinations, user profiles, and the user contexts. The proposed semantic web service ontology (OWL-S) is extended with context information taken into account. This architecture is context-aware but suffered from a semantic-based quality evaluation for achieving an efficient framework.

Boukadi and al. [START_REF] Boukadi | CWSC4EC : How to Employ Context, Webn Service, and Community in Entreprise Collaboration[END_REF] introduced a new context categorization suitable for the enterprise collaboration environment and proposed a context-aware web service ontology for describing and selecting web services enhances dynamicity and agility of collaboration. The proposed approach takes into account only contextual information necessary for web service collaboration, and not considers QoS parameters and their semantics for web services discovery and selection.

Nicolicin-Georgescu and al [START_REF] Nicolicin-Georgescu | An Ontology-Based Autonomic System for Improving Data Warehouse Performances[END_REF].exploited ontologies and ontology-based rules data for improving query response times in data warehouses groups by modifying cache memory allocations. This work ensures a better data warehouse management at a lower cost and enables autonomy by using autonomic loop control. However, this work concentrated only on service performance as indicator of the system quality and not considers other QoS parameters like security and dynamic adaptation. Wang and al. [START_REF] Wang | A QoS-aware Selection Model for Semantic Web Services[END_REF] present a QoS model for service selection. This work is based on QoS ontology, which introduces a number of specific quality attributes and their respective quality measurements used for web service quality evaluation and selection. The authors have presented an interesting approach for annotating service description with QoS data. However, neither various communication protocols, not heterogeneous services are discussed. Therefore, it does not provide support for dealing with the dynamic service selection in order to adapt to highly varying context conditions. Moreover, proposed intelligent mechanisms (e.g. intelligent software agents) and techniques can be exploited in the practice of a new tourism quality management policy [START_REF] Handler | Agents and the Semantic Web[END_REF].

Currently, the semantic web service ontology (OWL-S) does not include a semantic description of contextual information. Thus, in its current state, it does not enable web business quality selection in pervasives computing environments. Moreover, proposed intelligent mechanisms (e.g. intelligent software agents) and techniques can be exploited in the practice of a new QoS policy [START_REF] Handler | Agents and the Semantic Web[END_REF].

Recently, Kerses and al. [START_REF] Keskes | Web Services Selection Based on Context Ontology and Quality of Services[END_REF] proposed a model that makes an automatic selection of best service provider that is based on mixed context and QoS ontology for a given set of parameters of QoS. It approach provides interesting benefits for QoS enhancement, allows a well interoperability between heterogeneous services, but disagree in the automatic service selection using multi-dimensional QoS and did not address any issue related to service integration in the limited portable devices.

MMSA (MultiMedia Software Architecture) [START_REF] Derdour | UML-Profile for Multimedia Software Architectures[END_REF] is an approach based on multimedia software architectures. It proposes a generic solution to the problem of incompatibility of services, in term of media type to another, or of a media format to another format. This work treats syntactic interoperability between the heterogeneous services and did not take into account the semantics of services and that of the location, category, QoS and context constraint and providers.

More recently, Lamolle and al. [START_REF] Lamolle | MODA : une architecture multimédia dirigée par les ontologies pour des systèmes multimédia en réseau[END_REF] introduced an ontology framework for automatic networked multimedia systems deployment and configuration. Based on wellknown standards, this framework integrates multimedia, distribution and platform concepts. This work offering excellent quality service management and deployment but disagree in the complex semantic context matching.

The methods and contributions of this paper are different from above related works. Firstly, the paper exploits services selection of mobile devices for maximizing user QoS under resource constraint in mobile applications. Secondly, the paper proposes an intelligent business quality management system, which is based on the Context-aware Quality of Semantic Web Service Ontology (CxQWS). The success of this approach provides its members intelligent access to business quality relatively inexpensively. This approach provides improved performance, a dynamic adaptation of selection services based on context metadata parameters (user, environment, device, and service provider context) for the better flexibility. Above two contributions don't appear in other related works.

Uses and Reasons of Ontology Importance

To investigate the importance of ontology from the technical view, for four reasons:

1. Homogenization: describing different contextual tourism information and different qualities tourism service using the same ontology confers to the ontology a role of homogenization for at least the service quality and the context information that it describes [START_REF] Mizoguchi | Ontology Engeneering Environments[END_REF][START_REF] Gargentilla | A survey on ontologybased applications: e-commerce and knowledge management will deserve special attention[END_REF]. 2. Interoperability: ontologies provide a good way to treat semantic interoperability between the heterogeneous representations of web services and thus QoS parameters and context parameters of tourism services by sharing common representation and semantics [START_REF] Handler | Agents and the Semantic Web[END_REF][START_REF] Mao | An adaptive ontology mapping approach with neural network based constraint satisfaction[END_REF]. 3. Expressiveness: most ontology languages such as RDF and OWL-S with high expressiveness are efficient and perform much better for expressing semantics of various portable devices properties (PDA, smart phone, iPods and Palms) and various QoS parameters. 4. Easy service discovery and selection: ontologies make the discovery and the selection of web business quality services easier, faster and automatic. The main mechanism for service discovery is service registry and semantics can be used for the discovery of web business quality service registries and for the management of QoS parameters and context parameters. This is for a better service choice among several equivalent functionalities [START_REF] Handler | Agents and the Semantic Web[END_REF].

Objectives and Motivations

Currently, the web service was described with XML standards languages such as semantic web service ontology (OWL-S) that facilitates the web service discovery and selection. However, contextual information and mobility awareness is not considered by OWL-S. This results to basic drawbacks:

-The poor service quality results from employing syntactic and semantic description without semantic-based context technologies and semantic-based mobility information, -Difficulty in meeting service quality such as reliability and efficiency, -The quality management applications for tourism business depend on their hardware and software platforms. These disadvantages can be tided, if we extend OWL-S for matching user requirement with tourism destination specifications at the semantic level, with context informa-tion and QoS parameters both takes into account. Thus an efficient mechanism is provided making the web service of mobile application/system easy to discover and associate with the semantic information contained in the ontology. Our proposed ontology introduces semantics related to the web business quality and also semantics related to the context-aware service which is defined as set of semantic metadata (see Figure 1).Due to heterogeneous services as well as its various communication protocols (GSM, 3G, Bluetooth, etc.) can be specified more easily using a semantic representation to better support resource-awareness. The automated processing of web business qualities in mobile-based applications requires that the user should be able to locate a business quality through its meaning independent of which service context is evolved. In our case, we refer by context-aware QoS modelling the semantic web service ontology that will be used for matching user quality requirements with tourism destination specifications at the semantic level, with context information of heterogeneous resources taken into account. Since, there is a diversity of quality attributes with a variety of contextual information, we find several contextual languages such as WSMO [START_REF] Wsmo | Web Service Modeling Ontology[END_REF], CxS [START_REF] Turner | Context-mediated representation of intillegent agents[END_REF] (ConteXtual Schema), DAML-S [START_REF] Horrocks | CDAML+OIL : a Description Logic for the Semantic Web[END_REF], ConteXtML [START_REF] Rayn | ConteXtML : Exchanging Contextual Information between a Mobile Client and the FieldNote Server[END_REF], CxBR [START_REF] Gonzlaez | Context-based representation of intelligent behavior in training simulations[END_REF] (ConteXt-Based Reasoning), CWSC4EC [START_REF] Boukadi | CWSC4EC : How to Employ Context, Webn Service, and Community in Entreprise Collaboration[END_REF] (Context Web Service and Community Enterprise Collaboration), CA-Discovery (Context-awareness in Context Discovery) [START_REF] Suraci | Context-awareness in Content Discovery architectures[END_REF], and CxG [START_REF] Brezillon | Context-based modeling of operators Practices bu Contextual Graphs[END_REF] (ConteXtual Graphs). These languages provide the means for defining QoS and context in specific application domains such as pervasive and mobile computing. However, neither various communication protocols, not heterogeneous services are discussed. Moreover, mobility introduces higher degree of heterogeneity and dynamicity than traditional distributed systems. The semantic quality service discovery and selection becomes essential in order to adapt to highly varying context conditions.

CXQWS: CONTEXT-AWARE QUALITY WEB SERVICE ONTOLOGY

The user should be able to locate a service quality through its meaning, independent of what the service quality is called, which language is used, which service context is evolved, and who the service provider is. Not only the QoS of different services need to be compatible, but also the devices performing the service execution need to respect certain properties and constraints. The resource properties and constraints nature make service qualities management and sharing an even more difficult.

To exploit the context-aware service quality to their full potential, we need ontologies to describe their semantics. Any web service can be based on our proposed ontology Context-aware Quality Web Service Ontology (CxQWS). Our ontology aiming to well capturing resources-awareness and controlling quality of services at semantic level, to well share their devices properties and constraints, to provide a semantic service selection facilities, to adapt and personalize a service to a device's capabilities and to user preferences.

Based on the previsouly mentioned works, the quality of any mobile-based application can be represented on our ontology CxQWS, which is defined as set of metadata parameters. These QoS parameters are: 1) -customer care and attention, 2) -professional level of staff, 3) -presentation of food and drink, 4) -quality of food and drink, 5)safety and security, 6) -level of service, 7) -speed and efficiency of service, 8) -quality of facilities, 9) -variety of facilities, 10) -tourist information, 11) -accessories in bedrooms , 12) -decoration, 13) -availability of breakfast, dinner and room services, 14) -continuity of service, 15) -durability of service. Figure 1 presents the relevant classes of our meta-ontology. It allows the services providers to publish metadata about their services within their context constraints in the UDDI registry. Providers would instantiate the classes from the ontology and publish the resulting individuals as OWL files on their web sites. The CxQWS define common concepts as well as relations ships among those concepts to represent QoS parameters and context constraints. CxQWS distinguishes the following concepts:

service has a name, a version and unique ID.

-service_category = {hotels, hostels, motels, restaurants, cafés, camping, bungalows}. -context_expression: denotes an expression which consists of context parameter, logic operator and logic value. The quality services selection must take the capabilities and limitations of a mobile device into account as well as any user requirements or preferences with respect to the service. Context is any information can be collected from service (name, version, resources needs), from device (resources capacities), from environment (time, location) and from user (preferences). CxQWS uses these informations to select a service quality in a dynamic environment, by confronting a required context (user, service) for a provided context (mobile node). Figure 2 shows the basic subclasses of the class context of CxQWS ontology.

-context_environment: The context environment representing available information characterizing network connections, time, date and location constraints. Network connections connect hardware components (PDAs, smart phone) having a limited bandwidth. Due to heterogeneous services as well as its various communication protocols (GSM, 3G, Bluetooth, etc.) can be specified more easily using a semantic representation to better support resource-awareness. The location, the date and the time represent respectively the geographical location within the exactly period of time that the service opportunity should be accomplished. speed, battery energy, etc) and act as execution environment for web service. A resourceawareness about current usage of processing power, memory, etc. is a prerequisite to guarantee a minimum QoS. -context_service: Service capability describes the operational features of a web service. Service capability includes semantic attributes like preconditions, effects and post-condition. The service confidence level is very interesting in the case of having two web services providing the same functionalities. They may have varying values for their performance quality attributes.

OVERVIEW OF THE FRAMEWORK

The proposal framework is a generic, dynamic and interpretable system regarding the management of web tourist service qualities. The framework facilitates semantic discovery and interoperability of web services that manage and deliver parts of tourist service qualities (for future reservation). It offers two operations. The first is the find web services qualities. This takes as input the user query, then returns list of candidate web services from the web service registry. The second is to allow providers to publish metadata about their services dynamically and to define their own specializations of the default classes based on the ontology CxQWS as illustrated in Figure 1.

Our framework for the context-aware quality web service discovery, as shown in Figure 3, is a three layers architecture consisting of top-level layer dedicated to the semantic query layer. The middle layer consists in three basic agents which are the Quality Semantic Service Discovery Agent (QSDA), the Quality Semantic Context Evaluator Agent (QCEA) and the Quality Semantic Service Sensor Agent (QSSA). The basic layer is a group of web services which the sequel consumes the local web services exposed by every web services provider offering services qualities and contexts descriptions. The discovery and selection process is a 3-step process:

Step. [START_REF] Pashtan | CATIS: A Context-Aware Tourist Information System[END_REF] The mobile client has no previous knowledge about the discovery services. In addition, when a given service is provided by two or more providers within the same area of the mobile clients; a decision should be made to select the most appropriate service based on contextual description with a better quality of service. Users submit queries through the mobile client application interface. As soon as a user describes the service qualities queries, the QSDA initiate the web service discovery mechanism based on the functional constraints.

Step. [START_REF]The Protégé Ontology Editor and Knowledge Acquisition System[END_REF] The QSDA responsible to communicate with the corresponding web services in order to initiate the query and finally to present personalized results. This is made thanks to semantic-based matching discovery which the QSDA could exploit the categorization of the ontology hierarchy to find suitable matches. The QSDA captures user's goals from user's queries which express a user's intention that s/he expects to get the results from the framework. Each goal represents functionalities and nonfunctionalities that a particular business domain offers. The information in goal is semantically described in our ontology by the service properties (service_category, ser-vice_city, QoS parameters, multimedia_content). Each goal can be expressed as an AND-OR of simple expression. The QSDA identify web services which matches functional requirements and QoS parameters of user's queries. A list of matched web services is passed to the QCEA for further selections.

Step. [START_REF] Abowd | Cyberguide: a Mobile Context-Aware Tour Guide[END_REF] Depending on the resources availability on the mobile device, on the environment context and on the service provider context, the function of QCEA is to select a better service from the primary list identified in the previous step within the equivalent functionalities.

Step.3.1 The QSSA is validated to provide collections of context metadata (context metadata) based on the ontology as illustrated in Figure 1. In case of changes in the client context (i.e. less available memory, less processor power) or in the environment context (i.e. less bandwidth), the QSSA will be notified by a set of contexts metadata in order to dynamically skip the next selected service of the primary list of services offering the same service functionalities which requires less available resources.

Step.3.2. This step consists on matching context metadata of each service of the primary list of Step 1 with the device context parameters from the QSSA. The service selection are based on inference techniques and semantic interpretation to (1) detect context semantic matching between two services contexts based on their semantic representation (context type, context parameters and logical operators), (2) evaluate the resulting matching and (3) automatically add a service in the list of selected web services. In [START_REF] Verma | Semantic Matching of Web Services Policies[END_REF], this comparison uses a simple syntactic comparison of web services policies but our top level ontology resolves the semantic and syntactic heterogeneities that resulted from the different representation languages and service providers. This step returns a list of web services which provide a better service quality to a specific mobile client. Table 1 details the algorithm for the dynamic service selection described above.

APPLICATION SCENARIOS

An adventurous tourist is planning a backpacking trip and looking for new places to explore. His Smart phone with integrated GPS has become indispensable for finding nearby activities that match his interests and is handy for finding cheap accommodations. To facilitate quality service discovery, CxQWS ontology on the Smart phone provides a semantic markup of the user profile, the Smart phone context, and the link to the CxQWS ontology. Tourists want to find service qualities, in which their quality's profiles are expressed by a certain media types (e.g. video, image, audio, text). For example, tourists are interested for service quality profiles that are represented by image and graphics, while viewing impaired visitors are interested for service quality which represented by audio. The above requirement can be satisfied by using the semantic metadata multimedia_content.

First scenario: A first simple scenario, tourists having specific requirements want to find service qualities with combined criteria. For example, visitors want to find restaurants map, located in Setif, with high speed and efficiency of service, as well as statuary obligations. This query can be answered by using the following semantic metadata:

service_category, service_city, speed_and_efficency_of_service and statuary_obligations_of_ service. Our selection tool (Selector Tool) allows tourists to specify the type of service, its location and different quality of services parameters; the type of context is its properties (e.g. throughput and memory size). This query can be submitted as follows:

(service_category ="Restaurant") AND (multimedia_content="Image") AND (service_city="Setif") AND (speed_and_efficency_of_service = "high") AND (statuary_obligations_of_service ="True")

Second scenario: A second scenario, tourists want to find services all services on the Smart phone, that semantically matches the Smart phone context (i.e. the less available memory) and the environment context (i.e. the less bandwidth). The context constraint can be:

(context_type ="Client") AND (memory_size ="low") AND (context_type="Environment") AND (bandwidth ='128K')

Third scenario: when he asked for the restaurant map, the Quality Semantic Service Discovery Agent returned two services: a black and white map and a coloured map. To limit the search results when browsing the restaurant map on the Smart phone, only the first one is returned

TABLE 1 ALGORITHM FOR DYNMAIC SERVICE SELECTION
that semantically matches the Smart phone context (i.e. the less available memory) and the environment context (i.e. the less bandwidth). The context constraint can be:

(service_category ="Restaurant") AND (multimedia_content="Image") AND (service_city="Setif") (speed_and_efficency_of_service = "high") AND (statu-ary_obligations_of_service ="True") AND (context_type ="Client") AND (memory_size ="low") AND (context_type="Environment") AND (bandwidth ='128K')

Implementation of CxQWS

We have utilized Protégé [START_REF]The Protégé Ontology Editor and Knowledge Acquisition System[END_REF] to build the CxQWS ontology using RDF/S (Resource Description Framework) language in which each statement consists of a triplet (subject, predicate and object). In particular we have used the RDF schema to describe our ontology concepts and its relationships. We briefly illustrate selected features of implementation of our ontology through snippets of the RDF code. Fig. 5 illustrates our ontology.

Intelligent Software Agents in the Contextaware Quality Semantic Service

Soon, providers of web services will advertise their services and contexts constraints on the semantic web, so that intelligent software agents can find and adapt them dynamically. For, these agents (see Figure 3), the semantic web infrastructure will be based on the ontology as illustrated in Figure 1. This ontology would be published as OWL file. The ontology CxQWS would allow providers to instantiate the classes from the ontology and publish the resulting individuals as OWL files on their web services. Then, a semantic web service specialized in enter- tainment context could send out a crawler agent to collect the available activities.

If a user then asks for a specific type of restaurant for mobile limited device (low bandwidth, less memory, etc.), the software agent could exploit the categorization of the ontology hierarchy to find suitable matches, and call auxiliary web services.

Providers of web services cannot only publish their metadata dynamically, but can also define their own specializations of the default classes. For example, an ontology module could define chic_restaurant as subclass of restaurant, and put semantic restrictions on these classes to describe its characteristics. Then, if a software agent searches for restaurant facilities it would also find the instances of the subtype, and also learn that chic restaurants are more expensive than usual restaurant, because they involve the ability for guests to sing etc. When a chic restaurant service delivers a high density color image or video sequence, a notification was sent to the QSSA telling that there is not enough available memory to continue storing such an image. In this case, the QCEA will skip to the next available service offering the same image with requires less memory (due to different internal representation technique). Moreover, proposed intelligent software agents (i.e. QSDA, QCEA and QSSA agents) can be exploited in the practice of a new tourism quality management policy [START_REF] Handler | Agents and the Semantic Web[END_REF].

Experiments Framework

In this section, we evaluate the performance of dynamic mobile selection algorithms (SSA) for querying semantic web services into context-aware applications using our Java VM simulator. The experiments are conducted to compare our CxQWS selection algorithms (SCA) for querying of semantic web services into context-aware applications with QoS Broker algorithm [START_REF] Badidi | A Qureying Model for Service Selection of Multi-classes QoS-aware Web Services[END_REF], which is quality and distributed based service selection model for mobilebased application. The reason for choosing reference [START_REF] Badidi | A Qureying Model for Service Selection of Multi-classes QoS-aware Web Services[END_REF] as the comparison is that both our work and reference [START_REF] Badidi | A Qureying Model for Service Selection of Multi-classes QoS-aware Web Services[END_REF] provide a various selection policies based selection algorithm for mobile environment. We simulate a tourist case study (second scenario) for two approaches using our Java VM simulator and we have varied the user (and respectively, the mobile device) from 1 to 150. The users use the system as modeled by a Poison-process with an arrival rate of 10 per hour. There are two communication protocols between the server and the mobile client, either a GPS link, which has a rather a low bandwith, 3G link, which has a higher bandwith. The simulation took not more than approximately 10 seconds on a 3.5Ghz Pentium PC. In the simulator, different agents are used QSDA, QCEA, and QSSA. The framework dynamic service selection starts a listening thread that listens to the requests from users. It receives the requests of users and puts them into the queue. While the queue is not empty, the QSDA starts the dynamic service selection algorithm to find the right match.

Analysis and Discussion

The first experiment is to test convergence of mobile services selection algorithms (SSA) which includes quality service semantic level algorithm and resources-awareness semantic level algorithm. In Fig. 4, when user arrival number is low (n=15), the convergence iteration of SSA is one. When user arrival rate increases to 45, the convergence iterations of SSA increase to two. In task intensive case (n = 90), there are more request waiting for a match than the complete responses. When user arrival rate increases quickly, the match of the services will increase, and some users will not afford requests services.

The second experiment is to test performance of our CxOWS with QoS Broker. Figure 5 shows the evaluation results, meaning that our approach turns out to be the best. When user arrival rate is small, there are enough resources (e.g. network bandwith) for mobile users to select efficient service, and obtain better response times. Compared with QoS Broker, the response times of SCA increase slowly than QoS Broker when the user arrival rate increases. This result is practically significant as well related to the low adaptation effort e.g. takes into account all the heterogeneous services as its various communication protocols (GSM, 3G, Bluetooth, etc.) as consequences of self-selection for environment evolution (e.g. network bandwith) guided by the adaption policies.

CONCLUSION AND FUTURE WORKS

The paper proposed CxQWS ontology being an extension to the web service, allows for considering quality and resource-awareness while discovering and selecting web service. It allows web services to share their media resources using semantic representation to the end-user. Using quality and context parameters for describing and selecting web services enhances dynamicity and transparency to mobile applications. The paper proposed also an intelligent context-aware quality service management framework. This framework allows web services to interoperate both with internal and external applications using the semantic quality and context matching techniques. The idea of presented framework consists of two steps. At the first step, services are defined as set of semantic metadata, which reflect service requirements and QoS parameters. At the second step, services with a semantic contextual metadata are elaborated. Such a procedure ensures that the selection decisions should be based on the semantic quality representation of the created services. We presented application scenarios of our ontology. As a result, Therefore, CxQWS provides a great flexibility and enables intelligent adaptation and customization of mobile client software. In this paper we have presented tool for dynamic service selection and management (Selector) in order to explore service space at a semantic level. The most innovative characteristic of the tool is that it profits from the potential of ontological space (i.e. CxQWS) to achieve semantic quality and context-awareness while discovering and selecting web ser-For the near future, our tool will be evaluated on large-size systems with industrial partners and we plan to include refinements on the ontology proposed for the representation of the context-aware services qualities.

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  and service provider context) for the better flexibility, adaptation and customization of mobile client software.

  -service_city: the city which the service facilities are located. -QoS_parameters: the list of the QoS parameters. This class includes the above fifty basic subclasses. QoS parameters concern not only the services but also the device resources where the execution is taking place. -multimedia_content = {text, images, graphic, sound, video}. -context = {service provider, user, device, environ-ment} -context_parameters: Each context category has specific context parameters. For example, the devicerelated context is a collection of parameters (memory size, CPU power, bandwidth, battery lifecycle, etc). Some context parameters may use semantically related terms, e.g. CPU power, CPU speed. -context_constraint: consists of simple or composite context expression. For example a context constraint can be bandwidth=10000 or memory_size= 'less'.

  Context_environment: {Time, Location, Date, Net-work} -Network = {type, protocol, bandwidth} -type = {wired | wireless} -protocol = {GSM | 3G| WLAN | Bluetooth} -bandwidth = {128K|256K |512K | 256K | 256K |1M} -context_device: The hardware components are mobile devices like PDAs or smart phone, are constrained in their resources (memory size, CPU

Fig. 1 .

 1 Fig. 1. The structure of CxOWS meta-ontology.

Fig. 2 Fig. 3

 23 Fig. 2 Context types ontology.

Fig. 4 .

 4 Fig. 4. Research results for the third scenario.

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  ?xml version="1.0"?> <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xml:base="http://univsetif.dz/schemas/context_aware_qality_service_ontology"> <rdfs:Class rdf:ID ="QoS_parameters"/> <rdfs:Property rdf:ID ="Service_id"/> <rdfs:Property rdf:ID ="Service_category"/> <rdfs:Property rdf:ID ="Service_city"/> . . . <rdfs: Property rdf:ID="Has_QoS"> <rdfs:range rdf:resource="# QoS_parameters"/> <rdfs:domain rdf:resource="#Servie"/> </rdfs:Property > <rdfs: Property rdf:ID="Is_Associatedwith"> <rdfs:range rdf:resource="# Context"/> <rdfs:domain rdf:resource="#Servie"/> </rdfs:Property > <rdfs: Class rdf:ID ="QoS_parameters"/> <rdfs: Property rdf:ID ="Service_city"/> <rdfs: Property rdf:ID ="QoS_1"/> <rdfs: Property rdf:ID ="QoS_2"/> . . . <rdfs:domain rdf:ressource ="#QoS_values"/> < rdfs:domain rdf:ressource ="&xsd ;string "/> <rdfs: Class rdf:ID ="Context"/> . . . < rdfs:Class rdf:ID="ressource"> <rdfs:subClassOf rdf:resource="#Context_Device"/> <rdfs:Property rdf:ID ="Memory_size"/> <rdfs:Property rdf:ID ="CPU_speed"/> <rdfs:Property rdf:ID ="Battery_energy"/>

Fig. 6 .Fig. 5 .

 65 Fig. 6. response time under various device mobiles

TABLE 2 CXQWS

 2 ONTOLOGY USING RDF

ACKNOWLEDGMENT

We would like to thank our colleagues and students for testing SELECTOR tool and specially Hamza Reffad and Roumili. The authors appreciate the in-depth comments given by the anonymous reviewers to improve this work.

Dynamic Service Selection Algorithm

Input Goal = {g1,g2, g3,….gn} // a set of user's goals Service = {s1,s2,….sn} // a set of services Output MatchedService_List1   // a set of services that meet the user's goal MatchedService_List2   // a set of services that meet the user' Adel Alti obtained the Master degree from the University of Setif (UFAS), Algeria, in 1998. He is holding a Ph.D. degree in software engineering from UFAS university of Sétif, Algeria, 2011. Right now he is an associate professor at University of Sétif. He is a member of the research group LRSD. His area of interests includes automated software engineering, mapping multimedia concepts into UML, semantic integration of architectural description into MDA platforms, context-aware quality software architectures and automated service management, Context and QoS. During his work he has published number of publications concerning these subjects.

Abdallah Boukerram is an associate professor at university of Setif (UFAS), Algeria. He obtained his PhD degree in computer science from university of L. Pasteur, Strasbourg, France, 1991. He is interesting in Software Parallel Architecture, and Grid Computing. He is the President of the scientific comity of Computer Science Department (UFAS), where he is supervising many Master and PhD students. He is also the head of a research group in the Computer Science Department.

Roose Philippe is an associate professor at the LIUPPA/UPPA -FRANCE. He obtained his PhD degree in computer science from university of Bayonne, France, 2001. He head of the MOJITO and AEXIUM projects. His research interests are software architecture and platforms, pervasif and ubiquitous computing, mobility, software components services, context and QoS, muli-parts profiles. He is the co-author of three books on software component technologies.


Hessam Moussavinik
email: hessam.moussavinik@iet.ntnu.no

Wael Guibène
email: wael.guibene@eurecom.fr

Aawatif Hayar
email: aawatif.hayar@eurecom.fr

Centralized Collaborative Compressed Sensing of Wideband Spectrum for Cognitive Radios

Keywords: Collaborative compressed sensing, compressive sampling, cooperative, spectrum sensing, cognitive radio, distribution discontinuities, algebraic detection, wideband

This paper (1) presents a new centralized collaborative sensing technique for cognitive radio systems which combines algebraic tools and compressive sampling techniques. The proposed approach consists of the detection of spectrum holes using spectrum distribution discontinuities detector fed by compressed measurements. The compressed sensing algorithm is designed to take advantage from the primary signals sparsity and to keep the linearity and properties of the original signal in order to be able to apply algebraic detector on the compressed measurements. Collaboration among radios enables the cognitive network to detect hidden primary users and makes it more robust against fading and unknown channel conditions. Furthermore, as an important key point, collaboration makes it possible to sample more compressively at each radio, i.e., each radio performs sampling with a lower rate. The complexity of the proposed detector is also discussed and compared with the energy detector as reference algorithm. The comparison shows that the proposed technique outperforms energy detector in addition to its low complexity.

I. INTORDUCTION

Recently, compressed sensing/compressive sampling (CS) has been considered as a promising technique to improve and implement cognitive radio (CR) systems. Cognitive radio is an smart wireless communication system that is able to promote the efficiency of the spectrum usage by exploiting the free frequency bands in the spectrum, namely spectrum holes [START_REF] Haykin | Cognitive radio: brain-empowered wireless communications[END_REF], [START_REF] Mitola | Cognitive radio: making software radios more personal[END_REF]. Detection of spectrum holes is of the first steps of implementing a cognitive radio system. In wideband radio one may not be able to acquire a signal at the Nyquist sampling rate due to the current limitations in Analog-to-Digital Converter (ADC) technology [START_REF] Cands | Compressive sampling[END_REF]. Compressive sensing makes it possible to reconstruct a sparse signal by taking less samples than Nyquist sampling, and thus wideband spectrum sensing is doable by CS. An sparse signal or a compressible signal is a signal that is essentially dependent on a number of degrees of freedom which is smaller than the dimension of the signal sampled at Nyquist rate. In general, signals of practical interest may be only nearly sparse [START_REF] Cands | Compressive sampling[END_REF]. (1) The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement SACRA n ˚249060, SENDORA, and the SAM-POS/WISENET Project, which is funded by the Research Council of Norway under the contract number 176875/S10 and 176724/I30.

Apart from reconstructing the original signal, detection is more required and interesting in the context of cognitive radio. Generally, for detection purposes it is not necessary to reconstruct the original signal, but only an estimate of the relevant sufficient statistics for the problem at hand is enough. This leads to less required measurements and lower computational complexity [START_REF] Duarte | Sparse signal detection from incoherent projections[END_REF]. We do skip estimation of the original signal and instead we directly use the measurements for detection purpose, so the complexity of the system is reduced as much as possible.

It is known that a single cognitive radio may fail to detect a hidden node or primary user because of shadowing or being in deep fade. Collaboration among cognitive radios can greatly improve the detection performance of the cognitive network in such situations, and the probability that all radios be in deep fades becomes extremely low [START_REF] Mishra | Cooperative sensing among cognitive radios[END_REF]. The other significant key point, which we are interested in, is that collaboration among radios reduces the sampling rate at each single radio that leads to less complexity and lower energy consumption at each radio. Lower sampling rate corresponds to less hardware complexity, and transmitting fewer samples to the fusion center corresponds to less energy consumption.

In this paper we develop a centralized implementation of collaborative compressed sensing of wideband spectrum for cognitive radios that is combined with distribution discontinuities detection technique. In this sense a group of cognitive radios identify spectrum holes of a wideband spectrum with low complexity while the sensing action is robust to severe fading environments.

The rest of the paper is organized as follows. Section II states the motivation and system model. Section III details the proposed collaborative compressed sensing for spectrum detection. Section IV is dedicated to performance results, and section V concludes this paper.

II. MOTIVATION AND SYSTEM MODEL

A. Motivation

Cognitive radio (CR) as introduced by Joseph Mitola [START_REF] Iii | An Integrated Agent Architecture for Software Defined Radio[END_REF] is a self aware and intelligent device that can adapt itself to the wireless environment changes by first detecting them, and then adapting its radio parameters to the new opportunities. Cognitive radio technique is an smart wireless communication system that is able to promote the efficiency of the spectrum usage by exploiting the idle frequency bands in the spectrum, namely spectrum holes [START_REF] Haykin | Cognitive radio: brain-empowered wireless communications[END_REF], [START_REF] Mitola | Cognitive radio: making software radios more personal[END_REF]. Detection of spectrum holes is of the first steps of implementing a cognitive radio system. Different statistical approaches already exist. The easiest to implement and the reference one in terms of complexity is the energy detector (ED) [START_REF] Urkowitz | Energy detection of unknown deterministic signals[END_REF]. Nevertheless, ED is highly sensitive to noise and does not perform well in low signal to noise ratio (SNR). Other advanced techniques based on signals modulations and exploiting some of the transmitted signals inner features were also developed [START_REF] Sahai | A tutorial on spectrum sensing: fundamental limits and practical challenges[END_REF]. For instance, the cyclostationary features detector (CFD) exploits the builtin cyclic properties of the PU received signal. The CFD has a great robustness to noise compared to ED but its high complexity is still a consequent drawback. Other techniques that were developed by researchers at Eurécom Institute are based on model selection tools and entropy investigation [START_REF] Zayen | Blind spectrum sensing for cognitive radio based on signal space dimension estimation[END_REF]- [START_REF] Haddad | Cognitive radio sensing information-theoretic criteria based[END_REF].

An important issue in spectrum sensing by cognitive radio is the complexity that is introduced by sampling rate of received signal at a CR. Specially when the spectrum to be sensed is wide the sampling is more challenging. In wideband radio one may not be able to acquire a signal at the Nyquist sampling rate due to the current limitations in Analog-to-Digital Converter (ADC) technology [START_REF] Cands | Compressive sampling[END_REF]. Also, applying the mechanism of narrowband detector for wideband spectrum sensing is inflexible and slow. Therefore, efficient wideband sensing techniques are highly demanded to increase the sensing agility [START_REF] Quan | Collaborative wideband sensing for cognitive radios[END_REF].

Furthermore, collaborative sensing is generally required to detect hidden nodes to a CR, that is several CR nodes cooperatively sense the spectrum to detect primary users (PUs) and available holes. In such scenarios, the amount of data processing at each CR node, in both centralized and decentralized schemes, and the amount of data exchange between CR nodes and the fusion center in the centralized scheme are important factors in complexity and power consumption of the system. Also, high number of samples, sampled at Nyquist rate, increase the power consumption and complexity at CR nodes.

In order to actualize sensing in wide spectrum and to reduce the complexity and power consumption at CR nodes, sampling at a smaller rate than Nyquist rate, while reconstruction or detection of signal is accurately possible, is a prominent key. Hence, compressive sampling or compressed sensing (CS) becomes a promising solution in realization of cognitive radio and reducing the complexity and power consumption. Compressive sampling enables us to do the sampling at a smaller rate than Nyquist rate, sometimes much smaller, and accurately reconstruct the sparse signal, or perform detection or estimation.

The first step for cognitive radio is to sense the spectrum and identify the spectrum holes, or in other words, detect the occupied frequency bands. Typically the wireless signal in open access networks is sparse in the frequency domain since depending on location and at some times the percentage of spectrum occupancy is low due to the idle or not deployed radios [START_REF] Haykin | Cognitive radio: brain-empowered wireless communications[END_REF], [START_REF] Tian | Compressed sensing for wideband cognitive radios[END_REF]. For example, we can model the spectrally sparse wideband signals as

s(t) = N -1 j=0 β j e i2πjt/N , t = 0, • • • , N -1 (1)
where N is very large but the number of nonzero coefficients β j is much less than N . In this sense we can say that the signal is spectrally sparse [START_REF] Candes | An introduction to compressive sampling[END_REF]. Therefore, we would like to implement spectrum sensing in the context of cognitive radio by performing collaborative compressed sensing combined with distribution discontinuities detection. To avoid signal reconstruction burden we choose a sensing matrix that enables the algebraic detector properly works while accepting the compressed samples directly as input. Collaboration among cognitive radios greatly improve the sensing performance, while increasing the number of cooperative radios enables us for more reduction in the sampling rate at each individual radio, that is, more reduced complexity at each single radio.

B. System Model

We assume a centralized implementation where a group of collaborative cognitive radios are spread over a small area (∼ 1Km 2 ) and each of the radios communicates with the fusion center through the control channel. For the radio channel between primary users and cognitive radios we assume flat fading with a Rayleigh distribution and shadowing effect. Here, we assume a perfect channel between radios and fusion center.

Let us consider a discrete representation of the received signal at a single radio given by:

x(n) = A n s(n) + e(n) (2)
where A n is modeling the channel, s(n) represents the discrete signal, that is s(t) sampled at Nyquist rate, and e(n) ∼ N (0, σ 2 I n ) is i.i.d. Gaussian noise where I n is an identity matrix of size n.

For the moment, assume only an individual radio that samples the received signal at Nyquist rate, that is, it observes x(n). Then the radio sends its observations to the fusion center for being processed. We would like to distinguish between two classified hypothesis H 0 and H 1 :

H 0 : x(n) = e(n) (3) H 1 : x(n) = A n s(n) + e(n) (4)
where H 0 means that the sensed frequency band is white containing only noise and H 1 means that the sensed frequency band is occupied with a signal corrupted by noise. The key parameter of all spectrum sensing algorithms are the false alarm probability P F and the detection probability P D . P F is the probability to determine a frequency band as occupied while it is free, thus P F should be kept as small as possible.

P F = P (H 1 |H 0 ) = P (a signal (user) is present|H 0 ) (5)
P D is the probability to determine a frequency band as occupied when there exists a signal, thus P D should be kept as large as possible.

P D = 1 -P M = (6) 1 -P (H 0 |H 1 ) = 1 -P (no signal (user) is present|H 1 )
where P M denote the probability of missed detection. To design the optimal detector on Neyman-Pearson criterion, we try to maximize the overall P D under a given overall P F . In order to infer on the nature of the received signal, we calculate a threshold for each of the detectors. The decision threshold is determined using the required probability of false alarm P F given by [START_REF] Mishra | Cooperative sensing among cognitive radios[END_REF]. The threshold T h for a given P F is determined by solving the equation:

P F = P (a signal is present|H 0 )) = 1 -F H0 (T h) (7)
where F H0 denote the cumulative distribution function (CDF) under H 0 .

The algebraic approach is able to detect the signal distribution discontinuities and find their positions in the spectrum, having the complete signal (Nyquist rate samples) as input to the detector. But as we previously mentioned, the problem is that sampling a wideband signal with Nyquist rate is constrained due to the reasons highlighted in section I. In order to make the detection possible with less number of samples or smaller sampling rate, relatively to Nyquist rate, we implement compressed sensing technique. In this sense, by considering the sparseness of the signal, at each individual radio we observe the received signal compressively with a smaller rate than Nyquist rate such as:

y = Φx + e (8)
where y ∈ R M is the compressed measurements or observations, Φ is the sensing matrix, x ∈ R N is the received signal like A n s(n) as above, e is the additive noise, and M N . It is shown that with some conditions on Φ it is possible to recover x accurately based on y.

We would like to use the compressed samples directly to detect the frequency holes without recovering the signal itself. Since the algebraic detection of distribution discontinuities is a linear approach we find a proper sensing matrix that makes it possible to use the compressed samples as the input to the linear detector. Cognitive radios can use a same sensing matrix or different sensing matrices of each other. Here, we assume that radios use different sensing matrices of each other that are made randomly as explained in following section.

After compressive sensing, performed by each individual radio, each radio sends the compressed observations to the fusion center. It is clear that sending the compressed observations requires lower energy and smaller bandwidth for the control channel than the case with Nyquist sampling. In following section we discuss about compressed sensing and the proper type of the sensing matrix as well as the detection process at the fusion center.

III. COLLABORATIVE COMPRESSED SENSING FOR SPECTRUM DETECTION A. Compressive Sampling at an Individual Radio

First, let us give an introduction on compressive sampling. Let x ∈ R N be a signal with expansion in an orthonormal basis Ψ as

x(t) = N -1 j=0 α j ψ j (t), t = 0, • • • , N -1 ( 9
)
where Ψ is the N × N matrix with the waveforms ψ j as rows. To use convenient matrix notations we can write the decomposition as x = Ψα or equivalently, α = Ψ * x where Ψ * denotes conjugate transpose of Ψ. A signal x is sparse in the Ψ basis if the coefficient sequence α is supported on a small set. We say that a vector α is S-sparse if its support {j : α j = 0} is of cardinality less or equal to S [START_REF] Cands | Compressive sampling[END_REF]. Consider that we would like to recover all the N coefficients of x, vector α, from measurements y about x of the form

y m = x, φ m = N -1 n=0 φ mn x[n], m = 0, • • • , M -1 (10) or y = Φx = ΦΨα = Θα (11)
where we are interested in the case that M N , and the rows of the M × N sensing matrix Φ are incoherent with the columns of Ψ. Then it is shown that signal x can accurately and sometimes exactly be recovered, considering that the recovered signal x is given by x = Ψα , and α is the solution to the convex optimization program

min α∈R N ||α|| l1 subject to ΦΨ α = Θα = y ( 12
)
where ||α|| l1 := N j=1 |α j |. The compressed sensing (CS) theory states that there exists a measuring factor c > 1 such that only M := cS incoherent measurements y are needed to recover x with high probability. We also have to mention that except l 1 -minimization solution other methods such as greedy algorithms in [START_REF] Tropp | Signal recovery from random measurements via orthogonal matching pursuit[END_REF] exist for recovering the sparse signal [START_REF] Cands | Compressive sampling[END_REF], [START_REF] Duarte | Sparse signal detection from incoherent projections[END_REF], [START_REF] Candes | An introduction to compressive sampling[END_REF], [START_REF] Donoho | Compressed sensing[END_REF]- [START_REF] Candes | Stable signal recovery from incomplete and inaccurate measurements[END_REF].

In case of noisy measurements, i.e., y = Φx + e, where e is noise with ||e|| l2 ≤ , [START_REF] Candes | Stable signal recovery from incomplete and inaccurate measurements[END_REF] shows that solution to

min α∈R N ||α|| l1 subject to ||Θα -y|| l2 ≤ (13)
recovers the sparse signal with an error at most proportional to the noise level. Also, [START_REF] Candes | Stable signal recovery from incomplete and inaccurate measurements[END_REF] discuss the conditions for stable recovery from noisy measurements. Each single radio sends the compressed observations to the fusion center. In the fusion center the observations from each radio are processed separately by an algebraic approach to produce the detection result from each radio. Later, we explain how the detection results from all radios make the final detection of spectrum holes.

We are interested in doing the spectrum holes detection using algebraic approach directly from the compressed measurements without reconstructing the original signal itself. For this reason we must find out the appropriate sensing matrix according to the detection technique. The proposed detection technique is a linear algebraic algorithm. This technique uses the Fourier transform of the observed signal to detect the occupied frequency bands in the observed spectrum. Therefore the compressed measurements of the observed signal must keep the linearity and properties of the original signal in order to apply the detection algorithm successfully on the compressed measurements. In [START_REF] Hayar | Combined compressive sampling and distribution discontinuities detection approach to wideband spectrum sensing for cognitive radios[END_REF] we show that any row vector of the sensing matrix is a Dirac function, that is, only one column of each row is nonzero. To generate the sensing matrix we can start from generating Φ T matrix by randomly selecting M columns of an identity matrix I N . The sensing matrix, Φ, is given by transpose of Φ T , where the columns of the sensing matrix are unit-normed. So the sensing matrix Φ that we apply has a form like this

Φ ∼    0 1 0 • • • 0 0 0 0 . . . . . . . . . . . . . . . . . . . . . . . . 0 0 0 • • • 0 1 0 0    M ×N . ( 14
)
This form of sensing matrix gives us the opportunity to use the compressed measurements from each radio directly as input to the algebraic detection algorithm in the fusion center and thus avoiding the computation complexity of reconstructing the original signal. Radios can have a similar sensing matrix, but here we use a different random sensing matrix for each radio. Following, detection process at the fusion center is explained.

B. Centralized Detection at the Fusion Center

As we said, the compressed observations from each radio is processed separately at the fusion center with an algebraic approach. The algebraic detection (AD) is a new approach based on advanced differential algebra and operational calculus. In this method, the primary user's presence is rather casted as a change point detection in its transmission spectrum [START_REF] Guibene | Development and implementation of an algebraic detector for spectrum sensing[END_REF]. In this approach, the mathematical representation of the spectrum of the compressed measurements, i.e., the observed signal Y n from each radio in frequency domain, is assumed to be a piecewise P th polynomial signal expressed as following:

Y n = K k=1 Y k [n k-1 , n k ](f )p k (n -n k-1 ) + E n ( 15
)
where Y k [n k-1 , n k ] is the characteristic function, p k is a polynomial series of order P , E n is the additive corrupting noise, K is the number of subbands defined in the frequency range of observation interest, and n = f fs is the normalized frequency, where f s is the sampling frequency and f is the signal frequency.

Let us define the clean version of the received signal S n as:

S n = K k=1 Y k [n k-1 , n k ](f )p k (n -n k-1 ) (16)
And let b, the frequency band, is such that one and only one change point occurs in the interval

I b = [n k-1 , n k ] = [ν, ν+b], ν ≥ 0. Denoting S ν (n) = S(
n ν = 0 if S ν is continuous 0 < n ν ≤ b otherwise (17)
Then, the primary user presence on a sensed sub-band is equivalent to find 0 < n ν ≤ b on that band. The AD gives the opportunity to build a whole family of spectrum sensing detectors, depending on a given model order P . Depending on this model order, we can show that performance of the AD is increasing as the order P increases. The proposed algorithm is implemented as a filter bank which composed of P filters mounted in a parallel way. The impulse response of each filter is:

h k+1,n = (n l (b-n) P +k ) (k) (l-1)! , 0 < n < b 0, otherwise (18)
where k ∈ [0

• • • P -1] and l is chosen such that l > 2 × P . The proposed expression of h k+1,n , k ∈ [0 • • • P -1]
is determined by modeling the spectrum with a piecewise regular signal in frequency domain and casting the problem of spectrum sensing as a change point detection in the primary user transmission [START_REF] Guibene | Development and implementation of an algebraic detector for spectrum sensing[END_REF]. Finally, in each detected interval [n νi , n νi+1 ], we compute the following equation:

λ k+1 = nν i+1 m=nν i W m h k+1,m X m (19)
where W m is the weight for numeric integration defined by:

W 0 = W M = 0.5 W m = 1 otherwise ( 20
)
and M is the number of samples of the observed signal.

In order to infer whether the primary user is present in its sub-band, a decision function is computed as following:

D f = P k=0 λ k+1 (n ν ) (21)
The decision is made by comparing the threshold T h to the mean value of the decision function over the detected intervals.

In the collaborative sensing the final decision is made by applying a rule on the decisions from all the radios for each detected interval. Different rules maybe used, and here, we choose the averaging rule. Let us denote the detection results from collaboration by D. Decision function from each radio ρ on each interval [ν i , ν i+1 ] is denoted by D f ρ,νi . Then we have:

D ν = 1 Rs Rs ρ=1 D f ρ,νi (22)
where Rs is the number of collaborative radios. So, the final decision is made by comparing D to the threshold T h over detected subbands. In next section simulation results on the proposed technique are presented.

IV. SIMULATIONS

In this section we investigate the performance of the proposed collaborative compressed sensing with the energy detector (ED). ED is the most common method for spectrum sensing because of its non-coherency and low complexity. The energy detector measures the received energy during a finite time interval and compares it to a predetermined threshold. That is, the test statistic of the energy detector is:

M m=1 y m 2 (23)
where M is the number of samples of the received signal y from each radio.

Traditional ED can be simply implemented as a spectrum analyzer. A threshold used for primary user detection is highly susceptible to unknown or changing noise levels. Even if the threshold would be set adaptively, presence of any in-band interference would confuse the energy detector.

Since the complexity of sensing algorithms is a major concern in implementation and ED is well known for its simplicity, we choose ED as the comparison reference. Denoting N the number of Nyquist samples of the observed signal y and P the model order of AD, we show that the complexity of AD is P N and the complexity of ED is N . From these results, we clearly see that the exploited sensing algorithm has a comparable complexity to the energy detector. For the proposed AD based compressed sensing algorithm, the complexity is equal to: P M N N = P M , where M is the number of compressed measurements of the received signal and M N . Table I In order to achieve realistic and well founded simulations, DVB-T signals based on DVB-T 2K recommendations are used as the signals to be sensed. This choice can be justified by the fact that almost all licensed primary networks are DVB-T and secondary users are CR deployed in these networks. The signal and channel parameters are given in Table II M N = {10%, 20%, 30%}, is simulated and compared to the ED detector with single radio and no compression. Collaborative ED is not simulated due to timing issues and since the comparison is still valid with single radio because, as will be seen, performance of a single radio with compressed sensing for compression ratios of about 20% and higher is better than single ED with no compression. So, we expect that cooperative compressed sensing also ourperforms cooperative ED, intuitively.

Figure 1 shows the performance of energy detector (ED) with no compression and first order algebraic detector AD 1 with different compression ratios for collaborative groups of size 1, 5, 10 and 20 radios.

We note that only performance of a single radio with compression ratio of 10% is not as good as performance of ED with single radio and no compression. And for the rest of examples the performance is better. This is where the complexity of the compressed sensing, i.e., M N , is much lower than ED, i.e., N . Collaboration among radios greatly improve the detection performance. Also, we note that when the number of collaborations increases the compression ratio at each radio can be decreased in order to achieve a specific probability of detection P D . Figure 2 shows the probability of detection that is achievable by different number of collaborative radios for a compressed sensing ratio of M N = 10% at SNR=-20dB and P F = 0.05.

These results are obtained with Algebraic detection of order P = 1, where increasing the order to P = 2 improves the performance dramatically, while complexity increases to 2M from M . But, still, for example for M N = 10%, complexity remains much less than complexity of ED, i.e., N .

As a final word, we can say that the collaborative compressed sensing decreases the complexity and energy consumption of cognitive radio networks remarkably due to the low sampling rate required for each radio while it makes the cognitive network robust to fading.

V. CONCLUSION

In this work we present a new centralized collaborative sensing technique which combines compressive sampling and algebraic method to detect spectrum holes. We choose a compressed sensing matrix which keeps the linear properties of the sampled primary signal. Each radio of the collaboration group use a different random sensing matrix and sends its compressed observations to the fusion center. The fusion center process the compressed measurements from each radio separately with an algebraic detector. The fusion center makes the final decision by averaging over results from all the collaborative radios to localize spectrum distribution discontinuities and identify spectrum holes. The performance comparison at different sampling rates shows that the new designed scheme achieves better performance than energy detector while preserving a low computational complexity. Also, as an important key point, by increasing the number of collaborative radios the sampling rate at each radio decreases, in order to obtain a desired performance. This leads to a great reduction in terms of system complexity and implementation.

  n+ν), n ∈ [0, b] as the restriction of the signal in the interval I b and redefine the change point n ν relatively to I b such as:

Fig. 1 .Fig. 2 .

 12 Fig. 1.Probability of detection, P D , vs. SNR at P F = 0.05. CAD: Compressed sensing with Algebraic detection of order P = 1. SU: secondary users/collaborative radios.

  summarizes the complexity of each detector.

	Sensing technique	Complexity
	Energy detector	N
	Algebraic detector	PN
	Combined compressive sampling and	PM
	distribution discontinuities detection

TABLE I COMPLEXITY

 I COMPARISON OF THE THREE SENSING TECHNIQUES; M N .

TABLE II THE

 II

TRANSMITTED DVB-T PRIMARY USER SIGNAL AND CHANNEL PARAMETERS of 1, 5, 10, 20 radios. For each group size, compressed collaborative detection with different compression ratios,

CAD at 10% with 1 SU CAD at 10% with 5 SU CAD at 10% with 10 SU CAD at 10% with 20 SU CAD at 20% with 1 SU CAD at 20% with 5 SU CAD at 20% with 10 SU CAD at 20% with 20 SU CAD at 30% with 1 SU CAD at 30% with 5 SU CAD at 30% with 10 SU CAD at 30% with 20 SU Energy detector