Lucas Provensi
email: provensi@ifi.uio.no

Frank Eliassen

Roman Vitenberg
email: romanvi@ifi.uio.no

Romain Rouvoy
email: romain.rouvoy@lifl.fr

Improving Context Interpretation by Using Fuzzy Policies: The Case of Adaptive Video Streaming

Keywords: C.2 [Computer-Communication networks]: Distributed Systems adaptation, fuzzy rules, rate control

Adaptation is an increasingly important requirement for software systems executing in large-scale, heterogeneous, and dynamic environments. A central aspect of the adaptation methodology is management of contextual information needed to support the adaptation process. A major design challenge of managing contextual data lies in the fact that the information is partial, uncertain, and inherently suitable for diverging interpretations. While existing adaptation solutions focus on techniques, methods, and tools, the challenge of managing and interpreting ambiguous contextual information remains largely unresolved. In this paper, we present a new adaptation approach that aims to overcome these issues by applying fuzzy set theory and approximate reasoning. It proposes a knowledge management scheme to interpret imprecise information and effectively integrate this information into the adaptation feedback control loop. To test and evaluate our solution, we implemented it in an adaptation engine to perform rate control for media streaming applications. The evaluation results show the benefits of our approach in terms of flexibility and performance when compared to more traditional methods, such as TCP-friendly rate control.

INTRODUCTION

With the ubiquitous proliferation of large-scale software systems operating in highly dynamic environments, autonomous system execution in presence of everchanging operational settings and personalized user requirements becomes increasingly important. Design of modern software systems consequently emphasizes self-adaptive capabilities whose goal is to provide one or more of the self-* properties (self-healing, self-optimization, and self-protection).

In order to support the control tasks that constitute the adaptation process, there is a need to collect, store, and interpret relevant contextual information. A major design challenge of managing contextual data lies in the fact that the information is partial, uncertain, and inherently suitable for diverging interpretations. For example, media streaming applications may need to adapt the streaming protocol and its configuration parameters when the packet loss fraction becomes "high" or the amount of bandwidth available on a shared link becomes "low". Yet, the threshold for being high or low depends on a great deal of factors and settings that vary at runtime whereas the assessment of runtime parameters (such as a packet loss fraction or available bandwidth) is essentially partial and imprecise. This challenge is further aggravated by the need to maintain the adaptation process over time and evolve its individual elements independently of the rest of the system: When new versions of the streaming protocol become available, it may be beneficial to start using them under the same conditions on the packet loss and bandwidth. Monitoring of runtime parameters may need to be dynamically adjusted. New rules that trigger the adaptation can be added on the fly. If multiple rules can be applied to the same situation, relative weights may need to be assigned and dynamically adjusted. If the network link is upgraded or if the media streaming application updates the requirements (e.g., because of switching to a different encoder or a different video client), this will affect the interpretation of "low" bandwidth for the same rule.

In this paper, we propose a complete yet highly modular approach for developing adaptive systems that facilitates interpretation of imprecise or vague contextual information. The approach is based on applying fuzzy set theory [START_REF] Zadeh | Fuzzy sets*[END_REF], which allows the separation of knowledge from its interpretation, so that the interpretation can be independently formulated and updated. Additionally, the methodology comprises a variation of the well-known MAPE-K control loop [START_REF] Kephart | The vision of autonomic computing[END_REF] adapted for dealing with fuzzy knowledge. The MAPE-K model aims at separating the concerns of adaptation process from the application logic, improving the reusability of the middleware framework. The modularity of our approach promotes individual evolution of different aspects of knowledge management: formalization of a shared vocabulary, imprecise interpretation of concepts, definition of strategies/policies, and specification of the adaptation mechanism, as we further discuss and illustrate in Section 5.

While there exist a few works that employ principles of the fuzzy set theory in connection to adaptation, each of these works only focuses on a particular adaptation aspect without considering how fuzzy set theory can be weaved into the entire methodology in a modular way. In [START_REF] Chauvel | Composition of qualitative adaptation policies[END_REF][START_REF] Yu | Fuzzy recommendation towards qos-aware pervasive learning[END_REF][START_REF] Li | A control-based middleware framework for quality-of-service adaptations. Selected Areas in Communications[END_REF] the authors implement heuristic controllers based on imprecise information. However, these controllers are based on fixed ad-hoc knowledge management, which might compromise the solution reusability and its ability to evolve. Fuzzy sets and fuzzy logic have also been applied to develop specification languages for presenting adaptation requirements and goals [START_REF] Whittle | Relax: Incorporating uncertainty into the specification of self-adaptive systems[END_REF][START_REF] Baresi | Adaptation goals for adaptive service-oriented architectures[END_REF]. While these languages provide a systematic way of describing uncertain knowledge, it may be non-trivial to implement them and support these goals at runtime.

In order to validate the proposed methodology, we have integrated it into a adaptive video streaming application. We demonstrate how the methodology allows the construction of an adaptation engine that effectively controls the streaming rate according to bandwidth availability. To concretely show the efficiency of this engine, we evaluate its performance through a set of simulations (using the ns-2 network simulator), and compare it with an alternative implementation that uses the standard network level TCP Friendly Rate Control (TFRC) protocol [START_REF] Handley | Tcp friendly rate control (tfrc): Protocol specification[END_REF]. The simulation results confirm that our adaptation method is capable of producing smoother bitrate changes while keeping the loss rate acceptable. We also evaluate the benefits of the achieved bitrate in terms of the quality of the corresponding video stream according to a set of standard metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Considering the same encoding reconfiguration method for both protocols, we show that the video streams produced using the proposed fuzzy logic control generally achieve a 10% to 30% better quality than the video streams produced using the TFRC protocol.

APPLICATION SCENARIO

The application scenario that is used throughout this paper consists of a video streaming application, in which client nodes can connect to media servers to receive video streams in real time. Figure 1 shows the main components executing on the server and client nodes. This illustration is representative of many existing media streaming applications (e.g., VLC Media Player and Darwin Streaming Server) and the components are based on commonly used technologies and protocols. The server is composed of an H.264 [START_REF] Wiegand | Overview of the h. 264/avc video coding standard[END_REF] component, responsible for encoding raw video data into Network Abstraction Layer (NAL) units, suitable for streaming, and a Real Time Streaming Protocol (RTSP) [START_REF] Schulzrinne | Real time streaming protocol (rtsp)[END_REF] component, that packs and transmits the NAL units to client nodes. During a streaming session, RTSP components exchange Real-Time Transport Protocol (RTCP) reports, which contain useful information for controlling the session quality, such as packet loss fraction and inter-arrival jitter.

Since clients and server may be connected through a network with variable bandwidth availability, the application needs to continually adjust the streaming rate to match the network characteristics. This paper evaluates adaptation scenarios that focus on the bitrate produced by the encoder component as the primary quality dimension. Nevertheless, the proposed methods for knowledge management in Section 3 and control loop construction in Section 4 are generic enough, and can be used to implement new adaptations (or extend the existing ones) that consider other video quality dimensions, such as resolution and frame rate.

There are different approaches for implementing rate adaptation in this type of application. One approach is to rely on network level protocols for congestion control (such as increase/decrease algorithms) to obtain a target bitrate, and adapt the encoder accordingly [START_REF] Rejaie | Layered quality adaptation for internet video streaming[END_REF]. Protocols such as TFRC, use loss rate, round-trip time and packet size as parameters of throughput equations, designed to obtain a more accurate estimate of the available bandwidth. TFRC is arguably the best option for media streaming, however its throughput equation cannot be easily modified to reflect specific features of the application model (such as encoder tolerance to packet loss). Another approach is to completely implement the adaptation at the application level, with algorithms to scale up or down the video quality based on different network conditions, such as the one presented in [START_REF] Toma | Offering streaming rate adaptation to common media players[END_REF].

A fundamental problem when adapting this application (at the network or application layer) is the imprecision of the information used in the reasoning and decision making processes. First, it is not possible for the application to precisely calculate the currently available bandwidth, and this information needs to be inferred from context data fed back from clients to servers (e.g., through the mentioned RTCP reports), which in turn, may not be accurate. Second, the desirable state (adaptation goal) needs to be expressed in terms of imprecise context situations. For instance, does a packet loss measurement necessarily mean congestion? What is the acceptable loss fraction considering measurements inaccuracy? Therefore, the adaptation result may not be optimal (e.g., bitrate overshoots and oscillations). This problem is unavoidable, but it should at least be considered in the development process, so that the imprecision is made explicit in the system design and correctly reflected in the adaptation behavior.

KNOWLEDGE MANAGEMENT

In this section, we present our approach for systematically organizing the adaptation knowledge. As discussed in Section 1, we have augmented the knowledge management of a MAPE-K loop to support imprecision by applying fuzzy set theory. The traditional set theory, where an element belongs to a set or not, assumes a precise model with no ambiguities and well known parameters. In cases where these assumptions do not hold, which is often the case for adaptive systems with partial knowledge, we suggest the use of fuzzy set theory to quantify the model's imprecision. In the fuzzy set theory, an element belongs to a set to some degree, defined by a membership function. Consider, for instance, the statement: "The packet loss fraction is high". Using traditional set theory, a given fraction belongs either to the low set or to the high set (boolean membership relation). Using fuzzy set theory, since high is a subjective concept and cannot be precisely determined, a given fraction belongs to the high set to some degree, but at the same time, it can also belong to the low set to some degree (fuzzy membership relation).

Structuring Application Knowledge

Most adaptive systems employ ad hoc solutions for knowledge management, where the knowledge is scattered throughout different models, making it difficult for independent entities (such as adaptation managers and individual control tasks) to share and reuse common concepts. In our ap- proach, the knowledge is described using domain specific ontologies, which help in solving issues such as ambiguity, data organization and semantic interoperability. The result is a shared vocabulary, describing all the important concepts in the application domain. The vocabulary can be enhanced with imprecision and vagueness, by associating the interpretation of specific concepts with fuzzy sets.

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Figure 2 shows a concept from the vocabulary used for the streaming application. The event concept corresponds to any relevant information observed by the adaptation engine. The loss event concept describes information about packet losses experienced by the application, which is used by the adaptation controller to reason about the network condition. The loss fraction property indicates the fraction of packets that were not received by the client since the last report. This vocabulary is then extended with fuzzy predicates, so that the loss fraction property can be classified as high, low and acceptable (with different confidence levels obtained by applying the corresponding membership functions).

Figure 3 shows the membership functions used to interpret the loss fraction concept, corresponding to the predicates in Figure 2. In this case, we have used piecewise linear functions for simplicity, although other types of functions can be used (Gaussian, sigmoidal, etc.). Piecewise linear functions are easier to specify and computationally efficient (the imprecision regions between no membership and full membership are captured by simple linear functions), and are frequently used in fuzzy systems.

It is important to notice that membership functions represent the developer's knowledge about the application domain. The developer of the adaptive streaming application, for instance, knows that 10% is definitely a high value for loss fraction and 0% is a low value. He/she also knows that, in some cases, values between 0 and 2% can be tolerated by the video decoder (although the exact values are uncertain), and uses a trapedoizal shape for the acceptable function, as shown in Figure 3. In later development phases, these functions can be fine tuned to reflect new knowledge and better understanding of the application behavior.

Fuzzy Adaptation Policies

The shared vocabulary constructed with fuzzy concepts Figure 3: Loss fraction membership functions allows the introduction of fuzzy logic into the adaptation reasoning process. Instead of using conventional boolean statements (e.g., loss fraction ≥ 2.0%), it is now possible to apply fuzzy logic statements containing linguistic predicates (e.g., loss fraction is high). Fuzzy statements are employed in our approach to create rules, which can be grouped into high-level policies to drive the adaptation process. For example, by using the loss fraction concept described earlier, a simple increase/decrease rate control policy could be built with the following set of rules: In the above rules, adjustment is the rate adaptation parameter (related to a reconfiguration method), and positive, negative and null indicate respectively an increase, decrease or no change in the rate. The evaluation of this set of rules will produce membership degrees for all three rate adaptation possibilities, that will finally have to be converted to a single crisp value through a process called defuzzification, discussed in Section 4.

We have extended this simple policy to implement a fuzzy proportional-integral-derivative (PID) controller to adapt the video streaming application. The proportional term is already captured by the increase/decrease rules, since the rate adjustment is proportional to the loss fraction. The integral term represents the accumulation of the losses over time, and can help in stabilizing the adaptation process. The derivative term represents the rate of change, and can make the adaptation more responsive to considerable loss increases. The integral and derivative terms are also described as properties of the loss event concept shown in Figure 2, and are associated to fuzzy predicates describing the membership to the appropriate fuzzy sets: positive and negative for the integral term (accumulated loss) and increasing and decreasing for the derivative term (change rate).

The complete set of rules is presented below. The rules are weighted (using the keyword WITH ) to give different importance to the PID terms, and round-trip time (RTT) information is included to prevent possible latency increase caused by buffering. Note that these rules use the same vocabulary defined in the domain ontology (Figure 2), and offer a description of the adaptation process that is linguistically close to how a human would describe them. Also, since the only numerical values used are the relative weights, the set of rules is likely to remain the same as the application evolves, while the membership functions would be changing to reflect new knowledge with more precise values.

One recurrent problem with traditional adaptation policies is the possibility of conflicts, which is aggravated by the boolean nature of the decision making process. This problem can be alleviated with fuzzy logic, where the evaluation of each rule results in a degree of truth instead of true and false values, facilitating the choice and prioritization of the actions. In the application scenario, since the adaptation consists of adjusting only a single parameter, policies implemented as fuzzy rules are sufficient and conflicts are not likely to happen. However, the presented knowledge model also supports the use of fuzzy rules as an alternative way of expressing goal policies and utility functions, producing approximate solutions instead of optimal ones.

FUZZY ENGINE AS A MAPE-K LOOP

In this section, we propose a new effective way of introducing approximate reasoning into a MAPE-K adaptation loop, by exploiting the imprecise nature of fuzzy policies. To help us to describe and construct a control loop for the streaming application, we have developed a prototype of a flexible framework for adaptation, which is based on a conceptual model defined in a previous work [START_REF] Provensi | Towards a flexible and evolvable framework for self-adaptation[END_REF]. The framework provides support for the description and implementation of MAPE-K control tasks as semantic enabled services (using OWL-S notation [START_REF] Martin | Bringing semantics to web services with owl-s[END_REF]), allowing us to make design decisions at the level of individual control tasks. The adaptation loop is therefore described and executed as a composite service, which in this case implements a fuzzy inference engine capable of interpreting imprecise concepts and evaluating fuzzy policies. Figure 4 illustrates the adaptation loop where the steps of the fuzzy inference process are mapped to the appropriate control tasks of a MAPE-K loop.

The adaptation process assumes a shared knowledge base, as shown in Figure 4. We have implemented the knowledge The feedback control loop starts with the monitoring task, which aggregates context data received from sensors deployed in the application. For the streaming application, the sensors are simple plug-ins that intercept RTCP reports and forward them to the monitoring task. This task extracts the context information from the reports (loss fraction and RRT), derives other required inputs (accumulated loss and change rate) and produces the events consumed by the analyzing task. At this point, the loss event contains only crisp (i.e., raw) numerical values for its properties.

The analyzing task is responsible for the inference process, converting the crisp context data into membership degree values (fuzzification) and evaluating the rules. Events are interpreted according to the shared vocabulary, and the fuzzy predicates (with the appropriate membership function) are applied to obtain the membership degrees. After evaluating all the rules, the results should be combined in order to obtain a single membership value for each possible action (i.e., the results of rules 1,2 and 3 need to be combined to obtain the membership degree for the positive set). The combination is done by using the rule weights in a sum function that aggregates the membership degrees for each action. This aggregation method is closer to the PID method, but can also be configured statically or dynamically, as a parameter to fine tune the adaptation loop. Yet another configurable parameter is the method used to implement the fuzzy operators. We have applied the traditional min and max implementation of the AND and OR operators, respectively [START_REF] Zadeh | Fuzzy sets*[END_REF].

The planning task determines what action should be taken, considering the membership values of all possible actions provided by the analyzing task. The planner implements the defuzzification method, which is also a configuration parameter of the control task. There are many defuzzification methods that can be applied, such as center of mass, cen-ter of gravity and diverse maxima methods [START_REF] Zadeh | Fuzzy sets*[END_REF]. There are also different criteria for selecting the appropriate method, such as continuity and computational efficiency [START_REF] Leekwijck | Defuzzification: criteria and classification[END_REF]. For the application scenario, we have selected the center of gravity (COG) method as it satisfies the continuity criteria. This criteria is important for control systems, and states that small variations in the inputs should not cause big changes in the output. With COG, the output of the planning task is affected by the membership values of all possible actions, and not only by the one presenting the maximum degree.

Once the planner finishes the defuzzification process, it notifies the acting task of the selected action and the final crisp value (percentage to increase or decrease). The acting task executes the adaptation through a set of actuators plugged into the application. If the resulting output belongs to the null set, then no action is taken. If the output belongs to the positive or negative sets, the actuator is invoked to apply the rate adjustment using the reconfiguration method offered by the H.264 encoder. The described control loop is reusable and does not require any particular programming model, making its incorporation to existing systems easier. However, extra development effort is required to plug the controller into the managed system, as sensors and actuators are application-specific.

ADVANTAGES OF THE PROPOSED MOD-ULAR APPROACH

The modular nature of our approach enables the individual evolution of different aspects of knowledge management in adaptive systems. The first aspect is the shared vocabulary, which is specified as domain specific ontologies. Those ontologies can be formulated and evolved independently of the adaptation controller, and can be reused by various control tasks and in different adaptation policies. The loss event presented in Figure 2, for instance, could have been initially defined as containing only the Loss Fraction property, and later evolved (as a consequence of new requirements and adaptation knowledge) to include the Accumulated Loss and Change Rate properties, needed by the rules implementing the PID controller. Without using a modular approach, the adaptation knowledge would have been scattered among different policies and could have been described using incompatible vocabularies.

The second aspect is the interpretation of concepts. In our approach the fuzzy sets can evolve independently of the adaptation rules using them. One example is the acceptable fuzzy set shown in Figure 3: a particular loss fraction measurement can be interpreted as acceptable if it presents an high degree of membership to this set. By adjusting the membership function that defines the acceptable fuzzy set, the adaptation behavior can be changed to be less tolerant to packet loss, without the need to modify any of the adaptation rules. Using conventional adaptation rules and boolean logic, the interpretation is a fixed part of the rules, making it difficult to reason about and evolve adaptation knowledge that is inherently imprecise.

The third aspect is the specification of adaptation policies. By separating the interpretation of concepts from the policy definition, we make it possible to specify complete and semantically correct rules with only partial knowledge about the adaptation domain. The evolution of adaptation policies is then done at a higher abstraction level (with-out having to deal with numerical precision): Adding and removing rules, augmenting existing rules with new fuzzy statements and changing relative weights. As an example, the relative weights that have been assigned to the rules implementing the PID controller, can be modified in later development iterations to give different importance to the PID terms. This modification does not affect the interpretation of the concepts used by the rule, but just the final accumulated result of the rule set.

The fourth aspect is the specification of the adaptation engine. The modularity of our approach enables the definition of configurable MAPE-K loops with loosely coupled control tasks, that can be easily modified according to different application requirements. The adaptation loop implementing the fuzzy inference engine, for instance, can be reconfigured with different implementations for the accumulation and defuzzification methods. As an example, a more lightweight implementation of the adaptation loop could have been implemented by selecting only less complex and computationally efficient methods.

EVALUATION

In this section we present the evaluation of the proposed fuzzy rate adaptation method. The objective is to show the benefits that our approach brings to the application, in terms of bandwidth utilization and resulting media quality, when compared to a conventional implementation based on TFRC protocol. The evaluation is divided in two parts: Section 6.1 evaluates the adaptation loop performance at the network level and Section 6.2 evaluates the quality gain of the produced video streams. For the first part, we simulate different network conditions (using the ns-21 discrete event simulator), and compare the loss pattern and bitrates achieved by using the fuzzy method and the ones achieved by the TRFC protocol. For the second part, we have modified the video streaming application to reproduce the same network conditions by replaying the recorded simulation traces, resulting in adapted video sequences. The adapted video sequences are stored by the receiver, and we evaluate their quality using a set of standard video quality metrics.

Network behavior

For the first part of the evaluation, we extended ns-2 with the RTP and TFRC implementation proposed in [START_REF] Bouras | Extending the functionality of rtp/rtcp implementation in network simulator (ns-2) to support tcp friendly congestion control[END_REF], and with the adaptation mechanism presented in Section 4. We have used dumbbell topologies to simulate bottleneck links between a video stream sender and a receiver, as well as parking lot topologies, which introduce multiple bottlenecks with cross traffic traversing a number of intermediate routers. These topologies are commonly used to simulate bandwidthlimited bottleneck links. We have tested a broad range of network conditions, with varying link capacities (0.5 to 10 Mbits) and number of flows competing for the link (up to 20 flows).

Figures 5(a), 5(b) and 5(c) show the results of a simulation scenario involving the competition for the link capacity between a FTP flow (TCP), a constant bitrate flow (UDP) and the video stream (RTP). For this simulation, we have used the Linux implementation of the TCP protocol (TCP-Linux), with the new reno2 congestion control algorithm. The upperbound for the TCP congestion window was set to a value large enough to allow the fully utilization of the link. This scenario shows that the fuzzy method produces higher bitrates with less oscillations than TFRC, while keeping the loss fraction (0.49% avarage) close to the acceptable fuzzy set. However, it should be noted that when the fuzzy method is applied the bandwidth sharing is not fair, and at some point the RTP flow will be consuming more bandwidth than the TCP flow. If fairness is one of the application requirements, the adaptation rules and/or membership functions should be adjusted to achieve a fair behavior, otherwise the fuzzy method is not recommended.

Figures 6(a) and 6(b) show the average goodput (application level throughput) and loss fraction, achieved in simulations with fixed link capacity and increasing number of competing TCP flows. From the graphs it may be noticed that, with less competition and as long as the loss fraction is acceptable, the fuzzy method displays a more aggressive behavior than TFRC, achieving considerably higher goodput. With a high competition, the achieved goodput is lower than the one produced by TFRC, but the loss fraction is also lower, to fit the acceptable set.

A number of other simulation scenarios were evaluated, and the results were consistent with the ones presented in this paper. Figures 7(a) and 7(b) show the simulation results when we applied the parking lot topology. In this case, the traffic between adjacent routers is generated in bursts, with sending and idle times taken from a Pareto distribution. The bursts are intermittent and the traffic could be already interrupted when a rate adaptation occurs, which might result in inaccurate adaptive behavior. In addition, the context interpretation becomes more challenging as we add more intermediate routers, which increases the total latency experienced by the application and the chances of having a congested link in the path between sender and receiver. With increasing number of intermediate routers, the simulation results show that, even with intermittent traffic and increasing latency, the fuzzy method is able to keep the loss rate within the acceptable set and achieve a considerably higher goodput than TFRC in general.

Media Quality evaluation

In the second part of the evaluation, we generated adapted video sequences using the bitrate variations and loss patterns obtained with the simulations. We have used uncompressed video sequences 3 as input for the application. The encoding process is done at runtime to generate NAL units according to the target bitrate. When the bitrate is adapted, the encoder is reconfigured to reduce the encoding quality for the video frames while maintaining the same resolution and frame rate. The loss pattern is applied to the streaming ses- We applied a set of metrics to evaluate the video quality obtained with the fuzzy method in comparison with the quality obtained with TFRC. The chosen metrics were Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Spatial-Temporal SSIM (stSSIM), which considers temporal distortions, and the DCT based Video Quality Metric (VQM). PSNR is the most commonly used metric for the evaluation of the quality difference among video frames, but it is known to have poor correlation with subjective metrics (the ones based on human perception) [START_REF] Wang | Survey of objective video quality measurements[END_REF]. To have a more reliable indication of the video quality, we have also applied SSIM, stSSIM and VQM. Furthermore, we have shown that the intensity of the adaptation and the frequency of change per report interval are reduced when using the fuzzy method (as can be seen in Figure 5). The amplitude and frequency of adaptations are know to have significant impact on the subjective quality [START_REF] Ni | Flicker effects in adaptive video streaming to handheld devices[END_REF], thus we believe that our approach can improve subjective user satisfaction as well.

Table 1 summarizes the quality evaluation of video sequences obtained from some of the simulation scenarios discussed above. The values shown in the table are the average scores of the adapted video sequences compared with the original uncompressed sequences. Different portions of the sequence may present different scores, depending on the characteristics of the frames and the target bitrate. In general, the fuzzy method produces better video quality than TFRC according to all metrics tested. However, it should be noted that this is only possible with the appropriate decoding support (the H.264 decoder can tolerate NAL losses).

RELATED WORK

In this section, we present related works that apply fuzzy logic concepts to deal with uncertainty when specifying adaptive behavior and discuss how these works differ from our approach. In our previous short paper [START_REF] Provensi | Towards a flexible and evolvable framework for self-adaptation[END_REF], we outlined the idea of a flexible framework for self-adaptation. However, while [START_REF] Provensi | Towards a flexible and evolvable framework for self-adaptation[END_REF] discusses flexibility and evolvability at a conceptual level, this work is focused on a concrete approach for knowledge management using fuzzy set theory.

The RELAX language by Whittle et al. [START_REF] Whittle | Relax: Incorporating uncertainty into the specification of self-adaptive systems[END_REF], deals with uncertainty when defining adaptation behavior. The focus is on requirements specification using structured natural language, that can be relaxed at runtime based on environment changes. RELAX is in principle a language independent of specific adaptation mechanism, therefore a runtime system needs to be able to interpret and maintain the requirements. The work in [START_REF] Cheng | A goal-based modeling approach to develop requirements of an adaptive system with environmental uncertainty[END_REF] shows how RELAX can be integrated into the KAOS goal modeling framework. Nevertheless, the goal model still requires runtime adaptation support.

In [START_REF] Baresi | Adaptation goals for adaptive service-oriented architectures[END_REF], the authors show how an augmented KAOS model together with the RELAX notation could be integrated with a BPEL engine, to transform the system specification into running service compositions. These works deal with adaptation behavior specification at a different abstraction level (requirement elicitation) than ours, and we see them as complementary approaches, as the development process advocated by our framework can be modified to integrate structured languages used to specify requirements and goals.

The work by Chauvel et al. [START_REF] Chauvel | Composition of qualitative adaptation policies[END_REF] presents an approach for qualitative description of adaptation policies, specified at design time and interpreted at runtime. The policies can be composed using two different types of fuzzy rule sets: local reconfiguration rules, for adjustment of local system properties, and architectural reconfiguration rules, which describe the utility of a specific architecture reconfiguration. Other control-based approaches that make use of fuzzy set theory to deal with ambiguity and imprecision include [START_REF] Li | A control-based middleware framework for quality-of-service adaptations. Selected Areas in Communications[END_REF][START_REF] Yu | Fuzzy recommendation towards qos-aware pervasive learning[END_REF]. These works are similar to ours in the use of fuzzy rules for describing adaptation behavior, whereas no special attention is given to knowledge management. The adaptation loop is also implemented as a rigid controller, resulting in reusability issues and reducing the system's capacity to evolve.

Our work is also related to context awareness and more specifically to Quality of Context (QoC) [START_REF] Buchholz | Quality of context: What it is and why we need it[END_REF], which defines precision as a property of the context information. The problem of imprecision in context acquisition is aggravated when this information is aggregated from various sources, and the precision and accuracy of each sensor component need to be taken into consideration. In [START_REF] Zimmer | Qoc: Quality of context-improving the performance of context-aware applications[END_REF], fuzzy logic is used to obtain a reliability measure of aggregate context information. In our approach, the focus is on the knowledge imprecision as a consequence of human interpretation, and the membership functions represent the developer's understanding of the context situation (which includes his/her knowledge about sensor precision and accuracy). This way, we avoid the complexity of defining precision boundaries for individual sensors and managing reliability of aggregated context, while providing support to behavior evolution based of human knowledge.

Another relevant work, reported by Bridges et al. [START_REF] Bridges | Cholla: A framework for composing and coordinating adaptations in networked systems[END_REF], is a fuzzy-based framework to control a video streaming application. While the focus of this work is on strategy composition and coordination, less attention is given to knowledge man- agement and interpretation of contextual information. Furthermore, the authors only provide indications of the effectiveness of adaptation composition and coordination, but do not show any improvements regarding the video rate adaptation, since the application displays similar behavior as the increase/decrease protocol.

CONCLUSION

In this paper we have presented our approach for adaptation in presence of inherently imprecise/vague information. We have proposed a method for organizing knowledge and its interpretation, and demonstrated how to effectively integrate this knowledge into a MAPE-K adaptation loop. We have validated this approach for the case of video streaming applications, and we showed that it generally brings benefits to the application in terms of media quality. As future work, we intend to apply our approach to other adaptive applications, and also to investigate knowledge evolution at runtime. We believe that by dynamically changing the membership values, we can achieve more accurate situation dependent adaptations.

Figure 1 Figure 2 :

 12 Figure 1: Media Streaming Application

RULE 1 :

 1 IF loss_fraction IS low THEN adjustment IS positive; RULE 2: IF loss_fraction IS high THEN adjustment IS negative; RULE 3: IF loss_fraction IS acceptable THEN adjustment IS null;

Figure 4 :

 4 Figure 4: Control loop for the application scenario base as a simple repository service, that feeds adaptation rules to the control tasks forming the adaptation loop. The other elements of the shared knowledge, such as the domain specific ontology discussed in Section 3.1, are made available as web resources. The tasks are configured to use those resources as part of their own vocabulary, so they can correlate the linguistic terms used in the rules with the corresponding concepts in the ontology.The feedback control loop starts with the monitoring task, which aggregates context data received from sensors deployed in the application. For the streaming application, the sensors are simple plug-ins that intercept RTCP reports and forward them to the monitoring task. This task extracts the context information from the reports (loss fraction and RRT), derives other required inputs (accumulated loss and change rate) and produces the events consumed by the analyzing task. At this point, the loss event contains only crisp (i.e., raw) numerical values for its properties.The analyzing task is responsible for the inference process, converting the crisp context data into membership degree values (fuzzification) and evaluating the rules. Events are interpreted according to the shared vocabulary, and the fuzzy predicates (with the appropriate membership function) are applied to obtain the membership degrees. After evaluating all the rules, the results should be combined in order to obtain a single membership value for each possible action (i.e., the results of rules 1,2 and 3 need to be combined to obtain the membership degree for the positive set). The combination is done by using the rule weights in a sum function that aggregates the membership degrees for each action. This aggregation method is closer to the PID method, but can also be configured statically or dynamically, as a parameter to fine tune the adaptation loop. Yet another configurable parameter is the method used to implement the fuzzy operators. We have applied the traditional min and max implementation of the AND and OR operators, respectively[START_REF] Zadeh | Fuzzy sets*[END_REF].The planning task determines what action should be taken, considering the membership values of all possible actions provided by the analyzing task. The planner implements the defuzzification method, which is also a configuration parameter of the control task. There are many defuzzification methods that can be applied, such as center of mass, cen-

Figure 5 :

 5 Figure 5: Simulation results of TFRC (a) and fuzzy rate adaptation (b) for the scenario with competing TCP and UDP flows, and the observed packet loss values (c)

  Fuzzy Rate Adaptation vs. TFRC.

Figure 6 :

 6 Figure 6: Average goodput (a), and the observed packet loss (b) for simulations with increasing number of competing TCP flows.

3

  from http://media.xiph.org/video/derf/ Fuzzy Rate Adaptation vs. TFRC.

  tfrc (b) Observed packet loss.

Figure 7 :

 7 Figure 7: Average goodput (a), and the observed packet loss (b) for simulations with increasing number of intermediate routers in a parking lot topology.

Table 1 :

 1 Video Quality Evaluation

	Metric	Scenario 1 (No competing flows)	Scenario 2 (Figure 5)	Scenario 3 (10 TCP flows)
		Fuzzy Method	TFRC	Fuzzy Method TFRC Fuzzy Method	TFRC
	PSNR (higher is better)	33.07	29.77	35.02	32.77	26.17	25.24
	SSIM (higher is better)	0.91	0.82	0.97	0.89	0.76	0.78
	stSSIM (higher is better)	0.53	0.32	0.61	0.49	0.34	0.26
	VQM (lower is better)	1.69	2.20	1.52	1.58	2.31	2.53

http://www.isi.edu/nsnam/ns/

http://www.ietf.org/rfc/rfc2582.txt


Session: Domestic and international drivers of urban dynamics

Title: Urbanization and low-carbon growth pathways : Modeling the interactions between energy and real estate prices

Presenter: Henri WAISMAN (CIRED) This presentation proposes a framework for thinking the role of urban dynamics in the transition towards low-carbon societies.

The economic impacts arising from specific policy interventions aimed at CO2 abatement (mainly, carbon taxes/subsidies and emission trading permits) are conventionnally analyzed with multi-regional, multi-sectoral CGE frameworks. The development of those frameworks has been mostly oriented towards the representation of the energy sector with focuses on supply-side technologies, energy efficiency and structural change, and most energy-economy models consider carbon price as the only driver of decarbonizing economies. On the other hand, despite evidence of the specificities of the transportation sector (low price-elasticity but sensitivity to location decisions, spatial organization and infrastruicture availability), poor attention has been devoted to the representation of location patterns and non-energy drivers of mitigation costs.

Overcoming these limitations and representing the role of the spatial organization of the economy in long-term growth patterns raise number of methodological issues: i) capturing the features of second-best worlds with imperfect foresight, inertia of capital stocks, market imperfections (underutilization of production factors); ii) representing structural change driving the decoupling between growth and energy beyond pure energy efficiency as a consequence of the interplay between consumption styles (preferences), technical potentials (resource and technology availability, asymptotes) and location patterns; iii) capturing the interplay between energy prices, land prices and the growth engine (productivity, demography, savings) in an opened economy; iv) endogenizing the urbanization process and location decisions in urban/rural areas. This is done by enabling the dialogue between the general equilibrium framework Imaclim-R and a model of systems of cities in interaction. The former has been developed at CIRED for the analysis of long-term energy and climate futures in second-best worlds. It adopts dynamic recursive architecture, a dual accounting of energy and transport flows in physical quantities and money values, and embarks bottom-up expertise through reduced forms of technologyrich models. The model of urban economies relies on NEG principles but extends them to provide numerical analysis of location choices among multiple agglomerations and within urban areas, in line with both empirical evidence and microeconomic theory. These trends provide a spatial disaggregation of macroeconomic trends by representing explicitly agglomeration and land-use patterns as crucial determinants of mobility needs, economic activities and related negative externalities. , able to disaggregate spatially the economic activity into a set of urban agglomerations

The dialogue between these two models enables capturing both the driving effect of macroeconomic trends on urban dynamics and, conversely, the consequences of urban/regional location choices on mobility needs, investments and production possibilities affecting macroeconomic trajectories and carbon emissions. We use this integrated description of global macroeconomic trajectories and location decisions in multiple agglomerations to reassess the cost of climate policies when considering not only their global dimensions captured by carbon and energy prices, but also their local determinants associated to the spatial organization in urban areas. Among the latter, we focus more specifically on the interplay between constrained mobility needs corresponding to daily commuting distances, investment constraints due to housing and transport infrastructure deployment and land prices capturing the competition for different locations within agglomerations.

We analyze the effects of macroeconomic trends, oil price trajectories and technical change on the long-term dynamics of urban systems. We demonstrate in particular that the rise of energy prices increases commuting costs in urban areas and hence fosters a urban densification process during the next decades, whereas the diffusion of energy efficiency decreases the commuting costs and favors a dispersion of urban settlements in the long term.

When a climate policy is adopted, the carbon price follows a trajectory that can be distinguished in three phases. In the short-term, the carbon price increases rather sharply and reaches 100$/tCO 2 around 2030, because of the necessity to give a strong early signal to trigger emission reductions despite inertias on the renewal of technical systems and imperfect foresight. In the medium-term (2030-2060), the carbon price tends to decline, since carbon prices above 50$/tCO 2 are sufficient to reach most of mitigation potentials in industry, residential and power sectors, which form the core of emission reductions. In the long-term, the carbon price features a sharp increase in order to reach the remaining high-cost mitigation potentials (especially in the transport sector). The higher cost of fossil fuels (and hence of private transport) triggers a densification process in urban areas, which in turn tends to increase the price of land in urban areas.

We finally test the implementation of explicit policies at the urban scale, in the form of investments devoted to improve the compatibility of urban systems to the climate policy. These urban policies prove to reduce the cost of climate policy by facilitating the decrease of transport-related carbon emissions, but have the indirect effect of forcing further densification with losses of housing welfare. The overall effect however remains positive and such local policy adopted at the urban scale ensures a 15% decrease of mitigation costs.


Kenta Mori
email: kenta.mori@inria.fr

Oyunchimeg Shagdar
email: oyunchimeg.shagdar@inria.fr

Satoshi Matsuura
email: satoshi.matsuura@inria.fr

Manabu Tsukada
email: manabu.tsukada@inria.fr

Thierry Ernst
email: thierry.ernst@inria.fr

Kazutoshi Fujikawa
email: fujikawa@itc.naist.jp

Experimental Study on Channel Congestion using IEEE 802.11p Communication System

published or not. The documents may come

I. INTRODUCTION

Over recent years, the emphasis in intelligent vehicle research has turned to Cooperative ITS in which vehicles communicate with each other and/or with the infrastructure for enhanced safe, efficient, and comfort driving support utilizing diverse communications technologies including IEEE 802.11p [1] (which has been specified as ITS-G5 for the Europian usage), WiFi, 3G/4G, WiMAX. Nevertheless, because most of the road safety and efficiency applications require direct V2V communications, the IEEE 802.11p is expected to play the key role for such applications. The medium access control (MAC) of IEEE 802.11p adopts the Enhanced Distributed Channel Access (EDCA) defined in IEEE 802.11e [2], and the physical layer (PHY) adopts the Orthogonal Frequency Division Modulation (OFDM) used in IEEE 802.11a [START_REF]for Information technology -Telecommunications and information exchange between systems -Local and metropolitan area networks -Specific requirement, Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, High Speed Physical Layer in 5 GHz band[END_REF].

Since communications performance is largely affected by radio propagation characteristics and the channel condition, for a launch of ITS, it is necessary to have a detailed understanding of the performance of IEEE 802.11p. Indeed much work has been carried out to study the performances of PHY and MAC characteristics by theoretical analysis, simulation and testbed performances. The recent studies on outdoor signal propagation characteristics of the IEEE 802.11p show that the IEEE 802.11p can provide up to 1 kilometers of transmission coverage in V2I scenarios and stable communication can be achieved at low transmission rates: 6 to 9 Mbps [START_REF] Shagdar | Experimentation Towards IPv6 over IEEE 802.11p with ITS Station Architecture[END_REF]. Larger transmission coverage and lower data rate are desirable, especially for road safety applications, which require periodical message broadcasting that is not subject to frame loss detection and retransmission mechanisms at the MAC layer. A use of high transmission power (i.e., large transmission coverage) and low transmission rate can suffer from channel congestion problem more seriously compared to the case of low transmission power and high data rate. While channel congestion issues have been addressed in the literature mostly by theoretically and by simulation studies, we still miss experimental studies showing the seriousness of the issue. To this end, in this paper, we report our experimental study on the channel congestion issue and its dependency on the communication parameters, such as data size and data rate, using real multi-modal IEEE 802.11p testbed system. This paper is organized as follows. Section II highlights the existing ETSI standards and related works. In Section III, we report our experimental study on channel congestion and discuss about the achieved insights and future works Section IV. Finally we conclude the paper in Section V.

II. BACKGROUND

This section introduces research and standardization activities related with IEEE 802.11p communications. Then, we point out several issues on this communications.

A. Research and standardization activities

Cooperative ITS Applications: On a general assumption of ITS architecture, vehicles are equipped with the IEEE 802.11p in order to communicate between vehicles, and also between vehicles and the roadside (See Fig. 1). Vehicles can connect to the Internet via the roadside ITS Station. On the other hand, the ITS Station may be equipped with communication interfaces (e.g. 2G, 3G, Wi-Fi) that can connect to the general Internet infrastructure. A Vehicle ITS Station consists of a router (Mobile Router, MR) which is in charge of the communication of the entire ITS Station and hosts that are running applications. With this concept, the ITS Station hosts are free from managing the communication beyond the link where the hosts connect to, since the router is responsible for managing the communication of the entire ITS Station. The ITS applications running on the hosts are classified into three categories: traffic safety, traffic efficiency and infotainment. The definition of a Basic Set of Applications were defined in ETSI (European Telecommunications Standards Institute) [START_REF][END_REF]. Especially on the safety applications, low latency (e.g., less than 100 msec) communications or communication guarantee are needed.

IEEE 802.11p: The IEEE 802.11p [1] is a wireless medium access technology for V2V and V2R communications over the 5.9 GHz band. The IEEE 802.11p basically uses the same PHY as defined in IEEE 802.11a [START_REF]for Information technology -Telecommunications and information exchange between systems -Local and metropolitan area networks -Specific requirement, Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, High Speed Physical Layer in 5 GHz band[END_REF] but by default, it operates utilizing 10 MHz bands. The detailed specs of this band are shown on Table I.

The IEEE 802.11p is adapted by ETSI to European context through ITS-G5 standard [START_REF]ETSI TC ITS, Intelligent Transport Systems (ITS) European Profile Standard on the Physical and Medium Access Layer of 5GHz ITSs[END_REF]. According to the usages, ITS-G5 divides the band into three sub-bands ITS-G5A, ITS-G5B, and ITS-G5C, where 1 control channel (CC) and 5 service channels (SCs) are defined (See Table II). The control channel, G5CC, is dedicated to road safety and traffic efficiency applications. It can also be used for ITS service announcements. The service channels G5SC1 and G5SC2 are for road safety and traffic efficiency applications. Finally, the service channels G5SC3, G5SC4, and G5SC5 are dedicated to other ITS user applications. All ITS-G5 stations (STAs) operating on ITS-G5A and ITS-G5B are treated equally disregard whether they are mobile or fixed. For operations in ITS-G5C, a distinction between mobile and fixed STAs is made for spectrum management based on distributed frequency selection (DFS).

B. Channel congestion issues in IEEE 802.11p

The IEEE 802.11p employs the contention-based channel access EDCA (Enhanced Distribution Channel Access) which uses CSMA/CA (Carrier Sense Multiple Access with Congestion Avoidance). In EDCA, a node with a pending frame first senses to the channel for a fixed period of time, EIFS (Extended Inter-Frame Space), then transmits the frame if the channel was idle or defers the transmission for a random backoff time, which is set from the range σ ×[0, CW]. Here, σ is the slot time and CW is the contention window size. For the saturated scenario, i.e., nodes always have pending frames, the channel access probability, let us denote τ , is expressed simply as 1/(CW + 1) and the probability of successful transmission at a node is τ (1τ ) n-1 . Obviously, in order to avoid frame collisions consequently, channel congestion, CW should take on a large value when the number of contending nodes is large. Indeed, EDCA has a mechanism (so-called exponential backoff mechanism), which exponentially increases the CW in a given range of [CWmin, CWmax] after each consecutive transmission failure and resets the value to CWmin after a successful transmission. Unfortunately, for broadcast frame transmissions, which are the case for safety applications, the exponential backoff mechanism is not adopted and hence, CW takes always on CWmin). Adding to this, considering the strict delay requirement of safety applications, the IEEE 802.11p has set the default value of CWmin to relatively small values for the control channel: 15, 7, 3, and 3 for AC0, AC1, AC2, and AC3 access categories, respectively, for the control channel (CCH). Figure 2 shows the packet delivery ratio calculated following (1), which takes account of the transmission failures caused by collisions for the saturated scenario. The figure clearly shows the seriousness of the channel congestion issue.

PDR = nτ (1 -τ ) n-1 nτ . (1)
Much attention has been put the channel congestion issue in IEEE 802.11p. The authors of [START_REF] Torrent-Moreno | Fair sharing of bandwidth in vanets[END_REF] disclosed the relationship between the transmit power and channel load in VANET through an analytical model and further proposed a power control approach to achieve the fairness and avoid the channel congestion. However, the proposed approach requires tight synchronization among the nodes and a global knowledge on the channel load, which are difficult to achieve in the real-world systems. The authors of [START_REF] Baldessari | Joining forces for vanets: A combined transmit power and rate control algorithm[END_REF] proposed an approach that controls both the transmission rate and power based on the prediction of the number of the neighbors. While power control and rate control can be an efficient to avoid collision, it is often not desirable for safety applications, unless the system can efficiently react to the network and channel dynamics that can be extremely challenging task. A utilitybased congestion control approach is proposed in [START_REF] Wischhof | Congestion control in vehicular ad hoc networks[END_REF] for nonsafety applications. The main idea is to dynamically assign the bandwidth according to the utility value of the message to be transmitted at each device. Since this approach needs the road to be segmented into sections for calculating the message utility metric, it can not be directly used in context of safety applications.

As can be seen, while channel congestion is well considered and studied analytically and by simulations, we lack experimental studies that show the seriousness of the channel congestion in real IEEE 802.11p systems. To this end, the following sections introduce our experimental study and achieved insights regarding channel congestion and its dependency on the packet size and transmission rate.

DESCRIPTION OF LAGUNA LGN-00-11 OBU/RSU

Laguna is a multimodal communication device, software development and verification platform representing the state-of-the-art of ITS communications technology. Laguna is an all-in-one solution specializing in the design, integration and testing of communication protocols and solutions compliant with the latest development of vehicle and infrastructure communications worldwide. Laguna natively supports ITS communication architectures and standards developed by the joint efforts of ISO, CEN, ETSI and the IEEE. It aims to deliver services for hassle free implementation of experimental V2V, V2I and I2I communication scenarios, and as such, is an invaluable tool for test site development.

The Laguna platform complies with the latest standardized ITS Station Architecture, which builds on top of the family of the ISO CALM standards. The platform delivers reference ITS architecture functionalities for personal, vehicle, roadside and central ITS sub-systems.

The design also includes complete solutions for vehicle, roadside and central ITS infrastructure development using a powerful embedded computer platform that supports multi-modal wireless communication, including 3G and multi-channel 802.11p, and an IPv6 certified software system that is open and easily manageable by the centralized Laguna ITS Cloud Manager software, making any large scale deployment smooth, while reducing the overall operational costs at the same time. The design also includes complete solutions for vehicle, roadside and central ITS infrastructure development using a powerful embedded computer platform that supports multi-modal wireless communication, including 3G and multi-channel 802.11p, and an IPv6 certified software system that is open and easily manageable by the centralized Laguna ITS Cloud Manager software, making any large scale deployment smooth, while reducing the overall operational costs at the same time. The design also includes complete solutions for vehicle, roadside and central ITS infrastructure development using a powerful embedded computer platform that supports multi-modal wireless communication, including 3G and multi-channel 802.11p, and an IPv6 certified software system that is open and easily manageable by the centralized Laguna ITS Cloud Manager software, making any large scale deployment smooth, while reducing the overall operational costs at the same time. The design also includes complete solutions for vehicle, roadside and central ITS infrastructure development using a powerful embedded computer platform that supports multi-modal wireless communication, including 3G and multi-channel 802.11p, and an IPv6 certified software system that is open and easily manageable by the centralized Laguna ITS Cloud Manager software, making any large scale deployment smooth, while reducing the overall operational costs at the same time. The design also includes complete solutions for vehicle, roadside and central ITS infrastructure development using a powerful embedded computer platform that supports multi-modal wireless communication, including 3G and multi-channel 802.11p, and an IPv6 certified software system that is open and easily manageable by the centralized Laguna ITS Cloud Manager software, making any large scale deployment smooth, while reducing the overall operational costs at the same time. The design also includes complete solutions for vehicle, roadside and central ITS infrastructure development using a powerful embedded computer platform that supports multi-modal wireless communication, including 3G and multi-channel 802.11p, and an IPv6 certified software system that is open and easily manageable by the centralized Laguna ITS Cloud Manager software, making any large scale deployment smooth, while reducing the overall operational costs at the same time. The design also includes complete solutions for vehicle, roadside and central ITS infrastructure development using a powerful embedded computer platform that supports multi-modal wireless communication, including 3G and multi-channel 802.11p, and an IPv6 certified software system that is open and easily manageable by the centralized Laguna ITS Cloud Manager software, making any large scale deployment smooth, while reducing the overall operational costs at the same time. The design also includes complete solutions for vehicle, roadside and central ITS infrastructure development using a powerful embedded computer platform that supports multi-modal wireless communication, including 3G and multi-channel 802.11p, and an IPv6 certified software system that is open and easily manageable by the centralized Laguna ITS Cloud Manager software, making any large scale deployment smooth, while reducing the overall operational costs at the same time. The design also includes complete solutions for vehicle, roadside and central ITS infrastructure development using a powerful embedded computer platform that supports multi-modal wireless communication, including 3G and multi-channel 802.11p, and an IPv6 certified software system that is open and easily manageable by the centralized Laguna ITS Cloud Manager software, making any large scale deployment smooth, while reducing the overall operational costs at the same time. The Laguna platform complies with the latest standardized ITS Station Architecture, which builds on top of the family of the ISO CALM standards. The platform delivers reference ITS architecture functionalities for personal, vehicle, roadside and central ITS sub-systems.

The design also includes complete solutions for vehicle, roadside and central ITS infrastructure development using a powerful embedded computer platform that supports multi-modal wireless communication, including 3G and multi-channel 802.11p, and an IPv6 certified software system that is open and easily manageable by the centralized Laguna ITS Cloud Manager software, making any large scale deployment smooth, while reducing the overall operational costs at the same time. In this section, we describe how serious the packet collision issues is in VANET by examining how packet delivery ratio changes with the increase number of transmitting nodes using the indoor testbed. In VANET, broadcast is used as either discover neighbors or propagate useful traffic information to other vehicles located within a certain geographical area. The broadcast mechanism may cause the broadcast storm problem in a scenario in which there is a high level of contention and collisions because of an excessive number of transmission packets. On safety applications, the drop of packet delivery ratio indicates a increase in probability of traffic accidents. Therefore, we investigate the channel performances affected by number of transmitting nodes, bitrate, and packet size with broadcasting for safety applications.

In Europe, the message formats are standardized by ETSI for safety application using broadcast. Two representative message formats are shown below.

1) The Cooperative Awareness Message (CAM) is transmitted at all times to enable the ITS station to aware of other stations in its neighborhood area as well as their positions, movement, basic attributes and basic sensor information [START_REF][END_REF].

2) The Decentralized Environmental Notification Message (DENM) is transmitted to provide information of specific driving environment event or traffic event to other ITS stations with occurrence of their event [START_REF][END_REF].

We conducted evaluation with parameters which were determined on the assumption of using these message formats in this paper.

A. Experimental Settings

The equipment used in this experimental study is Commsignia LGN-00-11 (Laguna). The Laguna consists of an IEEE 802.11p compliant wireless interface card, embedded ARM11 platform and 128 MB RAM (See Table III). We implemented a network protocol stack combining IPv6 and GeoNetworking by using CarGeo6 0.8.9 on Ubuntu 10.04 LTS with Linux 2.6.35.13. CarGeo6 is an open source implementation of the standardized ITS station architecture for Cooperative ITS. We put all Laguna on the table (See Fig. 3). All nodes were included in communication range with each other. The prime objective of this paper is to investigate the influence of increase in number of transmitting nodes on the broadcast using IEEE 802.11p. We measured packet delivery ratio as metric in this evaluation. To perform these measurements, we used the iperf and tcpdump on each node. iperf generated traffic for 20 seconds according to the parameters of each scenario. Divided number of transmission packets by average of received packet which was captured by tcpdump on each node and we got packet delivery ratio. We increased number of transmitting nodes from 1 to 10 and measured the packet delivery ratio. Furthermore, we performed same experiments repetitively, setting the different combination of bitrate, packet interval and packet size (See Table IV). Besides, we used ETSI G5CCH for focusing on safety application [START_REF][END_REF].

B. Results

Minimum time interval between CAM generations is 100 ms [START_REF][END_REF] and the default bitrate of ETSI G5CCH is 6 Mbps [START_REF][END_REF]; therefore, we conducted the evaluation, setting packet interval to 100 ms and bitrate to 6 Mbps firstly (i.e., general traffic situation without specific driving environment event and traffic event). Figure 4 shows the packet delivery ratio of various packet sizes with 6 Mbps of bitrate and 100 ms of packet interval. The packet delivery ratio is high while number of transmitting nodes is small. Especially, there is almost no packet loss throughout the figure with 200 byte of packet size. However, the packet delivery ratio decreases in inverse proportion to the number of transmitting nodes. The longer the packet size gets, the more the packet delivery ratio decreases. Because the packet collision probability increase in proportion to the packet transmission time. As is mentioned above, minimum time interval between CAM generations is 100 ms. However, the packet interval decrease more by DENM generation with occurrence of specific driving environment event or traffic event. Figure 5 shows the packet delivery ratio of various packet interval with 6 Mbps of bitrate and 200 byte of packet size. The packet delivery ratio decreases in proportion to the packet interval. It is caused by the increase in number of transmission packets with the packet interval decreases. However, the result with 100 ms of interval and the result with 80 ms of interval are almost the same.

Figure 6 shows the packet delivery ratio of various packet interval with 6 Mbps of bitrate and 500 byte of packet size. There is almost no packet loss with a number of transmitting between 1 and 7 with 100 ms of interval, between 1 and 6 with 80 ms of interval, between 1 and 4 with 60 ms of interval, between 1 and 3 with 40 ms of interval. With 20 ms of interval, packet delivery ratio is low in spite of number of transmitting nodes. It is caused by the increase in number of transmission packets with the packet interval decreases.

Figure 7 shows the packet delivery ratio of various bitrate with 60 ms of packet interval and 500 of packet size. The packet delivery ratio decreases in inverse proportion to the number of transmitting nodes. However, the higher bitrate gets, the smaller such effect becomes. Because a packet transmission time decreases by using high bitrate.

IV. DISCUSSION

Our data show the channel congestion issue on IEEE 802.11p. Packet delivery ratio is affected by increase in number of transmitting nodes. This is attributed to the increase of packet collision probability which is caused by the increase in number of transmission packets. Furthermore, various transmitting parameters also effect the packet delivery ratio. In Fig. 4, the packet delivery ratio decreases in inverse proportion to the packet size. This is also due to increase of the packet collision probability. Because the packet collision probability increase in proportion to the packet transmission time. Of course, the packet transmission time increase by the increase of packet size. This result suggests that the packet collision occur probably if CAM packet size is 200 byte and there are over 7 nodes within communication range in general traffic situation without specific driving environment event and traffic event. In Fig. 5 and6, the packet delivery ratio decreases in proportion to the packet interval. The smaller packet interval gets, the greater number of transmission packets gets. This result suggests that the packet collision occur probably if CAM packet size is 200 byte, packet interval is over 80 ms and there are over 7 nodes within communication range. We assume that the packet collision increase by generation of DENM in a case with specific driving environment event and traffic event as compared with a case without such event because the number of transmission packets increases. In Fig. 7, the packet delivery ratio increases in proportion to the bitrate. Because the packet transmission time decrease with the bitrate increasing. This result suggests that the packet collision occur probably with CAM packet size is 500 byte and there are over 4 nodes within communication range, but there is almost no packet loss no more than 7 nodes if the bitrate is increased to 12 Mbps.

From the above, the packet delivery ratio was found to depend on number of transmission packets and packet transmission time. It is assumed that the packet delivery ratio is more lower in real environment because this results are gotten by indoor evaluation using nodes which don't move. On safety applications, the drop of packet delivery ratio indicates a increase in probability of traffic accidents. Therefore, we need to decrease the packet size, increase the packet interval, and increase the bitrate for road safety. However, decreasing the packet size and increasing the packet interval have limitations because the safety applications should send the necessary and sufficient informations on apposite timing to work effectively. In addition, high bitrate has not only advantage points, but also disadvantage points as compared with low bitrate. The higher bitrate is set, the more modulation technique complicates (See Table I). Complicated modulation techniques tend to have smaller communication range than simple modulation techniques because they are susceptible to noise [START_REF] Shagdar | Experimentation Towards IPv6 over IEEE 802.11p with ITS Station Architecture[END_REF]. Therefore, another drastic way is required for keeping the high packet delivery ratio on condition there is a lot of transmitting nodes.

V. CONCLUSION

In this paper, we investigated the packet delivery ratio as channel performance affected by number of devices, packet size and bitrate for Cooperative ITS applications. Our data show the channel congestion issue on IEEE 802.11p. On safety applications, the drop of packet delivery ratio indicates a increase in probability of traffic accidents. We need to decrease the packet size, increase the packet interval, and increase the bitrate for road safety. However, decreasing the packet size and increasing the packet interval have limits. High bitrate has small communication range as compared with low bitrate. Therefore, another drastic way is required for keeping the high packet delivery ratio. In our future work, we will consider how to keep high packet delivery ratio.

Fig. 2 .

 2 Fig. 1. ITS station reference architecture

Figure 1 .

 1 Figure 1. Laguna LGN-00-11 OBU/RSU

Figure 1 .

 1 Figure 1. Laguna LGN-00-11 OBU/RSU

Figure 1 .

 1 Figure 1. Laguna LGN-00-11 OBU/RSU

Figure 1 .

 1 Figure 1. Laguna LGN-00-11 OBU/RSU

Figure 1 .

 1 Figure 1. Laguna LGN-00-11 OBU/RSU

Figure 1 .

 1 Figure 1. Laguna LGN-00-11 OBU/RSU

Figure 1 .

 1 Figure 1. Laguna LGN-00-11 OBU/RSU

Figure 1 .

 1 Figure 1. Laguna LGN-00-11 OBU/RSU

Figure 1 .

 1 Figure 1. Laguna LGN-00-11 OBU/RSU

Figure 1 .Fig. 3 .

 13 Figure 1. Laguna LGN-00-11 OBU/RSU

Fig. 4 .Fig. 5 .

 45 Fig. 4. Comparison between various packet sizes (bitrate = 6 Mbps, packet interval = 100 ms)

Fig. 6 .Fig. 7 .

 67 Fig. 6. Comparison between various packet intervals (bitrate = 6 Mbps, packet size = 500 byte)

TABLE I MODULATION

 I AND DATA RATE FOR A 10 MHZ CHANNEL

	Modulation	Coding rate	Data rate [Mbps]
	BPSK	1/2	3
	BPSK	3/4	4.5
	QPSK	1/2	6
	QPSK	3/4	9
	16-QAM	1/2	12
	16-QAM	3/4	18
	64-QAM	1/2	24
	64-QAM	3/4	27

TABLE II

 II

		ITS-G5 CHANNEL ALLOCATION
	Sub-bands	Channel type	Centre frequency	Channel number
	ITS-G5A	G5CC	5900 MHz	180
	ITS-G5A	G5SC2	5890 MHz	178
	ITS-G5A	G5SC1	5880 MHz	176
	ITS-G5B	G5SC3	5870 MHz	174
	ITS-G5B	G5SC4	5860 MHz	172
	ITS-G5C	G5SC5	5470 to 5425 MHz