Ievgenii Tsokalo
email: ievgenii.tsokalo@tu-dresden.de

Stanislav Mudriievskyi
email: stanislav.mudriievskyi@tu-dresden.de

Ralf Lehnert
email: ralf.lehnert@tu-dresden.de

Development and calibration of a PLC simulation model for UPA-compliant networks

Keywords: Broadband Power line communication, UPA, TDMA, simulation model, test-field, ns3

Communication network of the Smart Grid is intended to serve a numerous amount of applications of different types. Its complicated structure often requires a utilization of several communication technologies: wireless, cable, etc. Power line communication (PLC) also remains a challenging solution for usage in this area. The present paper is targeted to explore the communication protocol of UPA (Universal Powerline Association) technology -one of Broadband PLC (BB-PLC) solutions, which are used today in the Smart Grid. In the paper the test field measurements of this technology are summarized and the development of the UPA simulation model is described. Among this description one of the most important stages of the simulation model design, i.e. calibration with the measurement results, is explained. The obtained simulation model is intended be used as a part of the PLC network planning tool.

Introduction

This paper presents the measurement results and the simulation model, which were obtained and built within the PLC4SG project [START_REF]PLC4SG project[END_REF]. This project is targeted to investigate two powerline technologies: UPA [START_REF]UPA model description[END_REF] and G.hn [START_REF] Hn | physical layer specification[END_REF] for Smart Grid applications in access networks. The former one, UPA, is considered in this paper. It was developed within the OPERA 1, 2 projects [START_REF][END_REF]. It is a broadband PLC solution with up to 200 Mbps data rate on physical layer. It maintains TCP/UDP IPv4 data transmissions and has a master-slave structure. The architecture of the measurement system is depicted in fig. 1. Hereinafter a master is referred to as a Measurement Computer (MC) and a slave -Terminal Device (TD). The PLC network can be managed either with the remote computer or with the remote desktop, connected though internet with the remote computer. The MC uses UMTS to get connected with the remote computer. Each device in the PLC network, MC and TDs, consist mainly of two parts: the actual PLC modem and the device, which runs the measurement software. We have selected GuruPlug computer for this purpose, which runs Debian as operating system [START_REF]GuruPlug product[END_REF]. The paper is based on already published measurement results and description of the measurement tool in [START_REF] Tsokalo | Investigation of Powerline Communication Modems for Energy Management Systems[END_REF][START_REF] Tsokalo | Measurement Software For Power Line Communication Network Traffic Analysis[END_REF]. The test field measurement consists of three test sites: urban, rural and industrial, located in Hanoi, Vietnam. Each of the test sites has up to 15 TDs and one MC. The network performance is estimated with the measurement of the network goodput under greedy traffic and the round trip time with a tool, described in [START_REF] Tsokalo | Measurement Software For Power Line Communication Network Traffic Analysis[END_REF]. The measurement of the traffic is executed with several routines: uplink and downlink point-to-multipoint and point-topoint measurements. In each point-to-point measurement the traffic between MC and one selected TD is transferred. The parallel measurement corresponds to starting the point-to-point ones with all TDs at a time. The packet length on TCP layer is kept constant throughout one measurement with one routine. The traffic with two packet lengths was tested: 100 and 1000 bytes. The developed tool calculates one data rate measurement value each second. The duration of each routine for each length of packet comprised several weeks. Alongside with the data rate measurement on the TCP layer, the physical layer data rates were measured with a special tool of the UPA chip manufacturer Marvell.

The conclusions on the obtained information from the measurement results are given in sect. 2. The description of the UPA simulation model is presented in sect. 3. The steps for the simulation model calibration with the measurement results are introduced in sect. 4. The last section describes the possibility of the available flexible channel model usage within the developed UPA simulation model.

Measurement Results

In the PLC4SG project the execution of the test field measurements had two main targets: estimation of the possible data rates in a real scenario and also the use of the statistical data for the calibration of the simulation model. Afterwards such model can be used within a network planning tool, helping to predict the network behavior in realistic scenarios. The measurements to allow investigate the following factors that can help in the analysis of the UPA protocol behavior: dependence of the data rate on the number of repeating hops between MC and TD; dependence of the data rate on the distance between MC and TD; point-to-point link quality with varying channel conditions; sharing the channel resources; system warm-up time; routing strategy.

We observed that the data rates of the point-to-point connections varied with the time of the day. This variation is caused by the channel conditions changing. It is clear that the PLC signal transfer function is not static and depends on impedances and noise of all electrical appliances, connected to the power grid. Our tests confirm the significant influence of electricity consumers on the PLC signal quality. The tool of the UPA chip manufacture Marvell for physical data rates measurement also allows read-out of the logical network structure, i.e. identification of the repeaters in the network. This structure was also changing depending on the time of the day.

The most noticeable time variation concerns the urban test field scenario. Also the measurements have shown that the PLC channel is highly asymmetric, so that the logical network structures in downlink and uplink are often different.

The accuracy of the measurement results is given by calculating the confidence intervals.

The detailed description of the test fields and measurement results can be found in [START_REF] Tsokalo | Measurement Software For Power Line Communication Network Traffic Analysis[END_REF].

Simulation Model

The ns-3 simulator [9] has been selected for implementation of the UPA simulation model, which layer structure is described in fig. 2.

Each layer implementation specialitites one can find in the following subsections. where the mean value of the data rate remains approximately constant without regard of that we take the values from all interval or from the half of it. It enables us to calculate the confidence interval for each period. We perform such division into period for each point-to-point connection separately for up-and downlink. The periods for different connections do not coincide.

The number of the measured values in a selected period depends on its length. The measured values of the physical data rates are gathered each 15 minutes. Each period can include up to (N • T P • /15) values, where N is the number of days, when the measurement was executed and T P -length of the period in minutes. The presented in fig. 3 measurement lasted 9 days. Each bunch of the measurement points in the selected time intervals we additionally divide into 20 groups with equal number of points. The groups are chosen in such a way that each one contains the points of the different time subintervals. These groups are called subruns, which we use for confidence interval calculation. The mean value and the standard deviation of the subrun mean values we save in the table for corresponding source and destination ID entry (Table 1 1 ).

Such table is created for each of the selected periods, covering all the time of the day. The simulation model uses different tables one after another. How the actual point-to-point data rate values are calculated is described in the following.

In accordance to the definition of the confidence interval the mean values of each subrun are independent random variables and can be described with the normal distribution. As mentioned before the introduced statistical model of the physical layer and channel has also short time variation of the data rates. Within each chosen period of the Table 1 the used in the simulation physical data rate vary with 1 minute interval. Each interval the actually used data rate value for every connection is calculated with the normal distribution taking the mean and the standard deviation values from the corresponding table.

We also assume that the transmission between a pair of nodes is always possible if the physical data rate between these nodes is more than the minimal one, provided by the UPA specification.

Of course the data transmission in real systems is characterized not only with a special data rate but also with the level of signal deterioration due to channel effects and the packet is considered to be received from physical layer if it is successfully decoded with redundancy codes. The simulation model simplifies this effect and the packet is considered to be successfully decoded if the table value of the data rate between the receiver and the transmitter is equal or higher than the data rate, with which the packet is actually sent.

MAC Layer

The scheduling algorithm defines the channel share depending on available information of demands -the values of data amount to transmit downlink to each destination and of each priority and uplink from each slave and of each priority. The master is the only modem that can initiate any transmission. It periodically asks the slaves to send the information about the length of their queues on all layers to all destinations and of all priorities. Keeping such information in memory the master runs a special algorithm to schedule the channel resources. OPERA simulation model uses fair share algorithm for traffic scheduling [START_REF] Phu Do | Scheduling Strategies for Adaptive Resource Sharing System-Application to the OPERA System[END_REF] and therefore this one is implemented in UPA model.

Logical Link Layer (LLC)

The data unit of the LLC level is called the burst. It is measured in a number of OFDM symbols and the symbols have constant time duration. The number of bits that can be packed in one symbol depends on the PLC channel quality.

LLC layer calculates the routing tables, which is also based on the current speed of physical connections. The UPA specification uses Spanning Tree Protocol for detection of the best routes. This protocol is described in the IEEE 802.1D standard. The best route selection is done first of all depending on data rates of physical connections, then on modems' IDs and other options, that define the privileges of one modems over others. We regard all modems "equal in rights" and concern only the first criteria. The calculation of the best route we explain with the example in fig. 4. This test scenario is not based on the measurement rsults and we have used it just to check the LLC layer functionality.

The picture describes the test scenario with 3 TDs. TD3 has different route in down-and uplink:

Layer Management Block

On MAC layer a special queue is created to store the MAC packet data units, that contain information from this block. This queue is served at the first priority level. Layer Management Block contains a number of services: All these services work to exchange the information between the modems about the neighbors, about the PLC connections quality and so on. The simulation model is constructed in such a way that all such information is already known by all the network members. It was not the aim to investigate the performance of these services. Therefore there is no necessity to implement them with their full functionality. Nevertheless such services are used by real modems and consume certain amount of the channel capacity. This amount can be calculated basing on OPERA specifications. Most of the services are periodic and the services' data units have almost not changeable size. Therefore it is possible to implement each service as a traffic consumer only, which sends packet of certain length with a certain period. The total amount of the channel utilization by the management block is estimated in the section 3.6.

Application Block

This block contains the source of simple and the greedy traffic. A simple traffic generator creates a fixed amount of packets at the simulation start-up. Greedy traffic generator cares about new packets generation each time, when the length of the convergence layer queues reach certain threshold value. The possibility to add other traffic generators is forseen.

Analysis of the Channel Resource Consumption

On the example of the test scenario fig. 4 we analyzed the overhead that is created by each protocol layer, when running the Parallel Down-and Uplink routines with a packet size of 1000 bytes. The summary is given in the Table 2.

Hereafter one can see that the Layer Management Block creates very little part of the total network traffic. MAC layer overhead consists of two parts: additional headers and the time, which is used to transfer the channel sharing information. The comparable with MAC part of capacity is consumed by physical layer. Therefore the total overhead takes about a half of the channel capacity.

Calibration of the simulation model

We have also designed the simulation scenario, which is analog to the real urban test case. Table 3 gives a characteristic of hop distances between the slaves and the master. The total number of modems in the test site is 15. From this table one can see how many slaves locate at distance of 1, 2, ... 6 hops from the master. The distance between directly (one hop) connected modems varies from 30 to 80 meters. As mentioned above during the test field two types of measurement data was gathered: data rates on physical and application layers. The calibration of the simulation model is done with the following principle: using the created statistical model of the physical layer model we should adapt the mechanisms of the Datalink layer in such a way, that on application layer we receive the data rates, which were received during the testfield measurements.

The calibration is proposed to be done in several steps, which are described below. This sequence of steps is chosen in such a way, that different protocol constants and algorithms can be calibrated independently from each other. For example, first of all we decided to compare the measurement and simulation results for a case, when the logical mechanisms of the communication protocol create the least overhead. In such a way we can consider only the calibration of the constants in the simulation (Subsections 4.1 and 4.2).

Round Trip Time without Traffic

The MAC scheduler updates periodically the information about the length of the slaves' queues. For this purpose it sends the special service MAC data units (SMPDUs). If there is no traffic present, the update period is constant. This constant is not provided by specification but we can estimate it with this routine. It should be directly proportional to the round trip time. With presence of other traffic this period can be extended because of two effects. At first, when the slaves send the MAC data units (MPDUs), they also write to the MPDU header the information, which is normally requested by SMPDUs. Therefore the master does not need to send SMPDUs itself so often as without uplink traffic. At second, when the time for the SMPUD sending comes the channel can be busy. In such case the SMPDU sending is delayed till the end of current transmissions. The extended value of the update period can be measured with round trip time during the network full load with traffic. This extended value of the period defines the maximal time that can be granted for one pointto-point transmission. This routine is described in subsection 4.6.

Point-to-point in downlink with 1 hop distance

With this routine we check the amount of headers of all protocol layers. We can do it only with this routines as far as with others the protocol creates much MAC overhead, that changes very dynamically. It originates because of the channel resource scheduling. For the selected routine it is also present but it does not change much and can be approximately calculated.

Point-to-point for multiple hop transmissions

During this routine it should be possible to see what is the priority of packets in repeating queues 2 in comparison to the application queues 3 .

Point-to-point up-and downlink routines

In comparison of up-and downlink routines it is possible to discover the algorithm with which the channel resources are shared between these types of traffic.

Point-to-multipoint routines

These routines reveal the information about the sharing the channel resources between different users. The relation between the queues of all types 4 can be estimated.

Duplex routines

This routine mainly serves as a verification of the previously obtained conclusions, i.e. the verification of the scheduling algorithm. Additionally as mentioned in subsection 4.1 this routine can be used for setting up the maximal time, which can be reserved for each point-to-point transmission.

Accurate channel model

As it was mentioned before we have used the statistical model of the physical layer and the channel, which is based on the measurement results. We considered it as the most accurate physical layer and channel model for the simulation model calibration. But its usage also has a significant drawback. We cannot simulate other scenarios, but only the ones, that were used during the test field 2 Repeater queue contains the packets, that are received by the modem from other modems and have to be repeated 3 Application queue contains the packet that were generated by the modem itself 4 In the convergence layer of each node there are two kinds of queues: repeaters and application. This layer has queues for all destinations and all priorities separately for both queue kinds measurements. Therefore after the calibration we replace this statistical model with another one, which allows the setup-up of arbitrary scenarios. This model was implemented in the network simulator 3 (ns-3) by the group [START_REF]Model of the PLC channel[END_REF]. It uses another way to model the PLC channel -the deterministic approach with the usage of transmission line theory. For details, please, refer to [START_REF]Model of the PLC channel[END_REF].

In order to use the channel model provided by this ns-3 module it is necessary to set up the parameters of the transmission medium. For this the impedances of the point (called outlet) where the PLC modems are connected to the line should be given. Furthermore the nodes in the network must be fixed with the coordinates and the connection cable type must be specified. In the provided module authors already included several types of cable. Afterwards the transmit power spectral density and noise floor is set and the channel transfer functions are calculated. Now the physical and higher layers can be connected to the channel and complete network can be simulated. With the given module it is also possible to change the impedances during the simulation what will cause the immediate recalculation of the transfer functions for the channel. Therefore the influence of the time-varying channel on the network performance can be produced. The test results with arbitrary simulation scenarios, which can be realized with a help of this channel model, are left for further study.

Conclusions

During the test field measurements the UPA technology has proved its stability in the work conditions of strong and fast PLC channel variation. All the PLC modems were reachable all the time, while the network logical structure was considerably changing.

The observation of the physical data rates of the point-to-point connections allowed the design of the simulation test scenario, which can be considered analog to the real test case. The obtained results of mutliple measurement routines on application layer give a possibility for accurate calibration of the developed UPA simulation model.

After the calibration phase the model can be connected with the accurate PLC channel model and can be used as a part of the network planning tool.

Fig. 1 .

 1 Fig. 1. Architecture of the measurement system

Fig. 2 .

 2 Fig. 2. Structure of the UPA simulation model

Fig. 4 .

 4 Fig. 4. Test simulation scenario

Table 1 .

 1 Fig. 3. Physical data rate of one point-to-point connection in one direction for 9 days of measurement Table example of the statistical values for the physical layer and channel modeling Source ID Destination ID Mean data rate Standard deviation

		65.91
		59.32
		52.73
	Datarate, Mbps	26.36 32.95 39.55 46.14
		19.77
		13.18
		6.59
		0.00	0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
					Time of day
			Measurement with SID: 6 DID: 1
			value	value	value	value

Table 2 .

 2 The overhead of the UPA protocol layers

	Layer	Overhead Downlink Overhead Uplink
	IPv4	1,2%	1,48%
	Convergence	1,5%	1,9%
	LLC	0,3%	0,26%
	MAC access time	20,53%	21,31%
	MAC headers	0,05%	0,05%
	PHY	21,5%	22,0%
	Layer Management	0,3%	0,3%
	Total	45,42%	47,29%

Table 3 .

 3 Hop distances in the real test case scenario Number of hops Number of modems in Downlink Number of modems in Uplink

Source and Destination ID values run through all possible combinations

Acknowledgments. The authors thank the following people: Marco Dietrich and Hendrik Schwuchow, Elcon Systemtechnik for providing the PLC modems.


Uwe Bauknecht
email: uwe.bauknecht@ikr.uni-stuttgart.de

Frank Feller
email: frank.feller@ikr.uni-stuttgart.de

Dynamic Resource Operation and Power Model for IP-over-WSON Networks

Keywords: Dynamic Energy Model, Resource Model, IP-over-WDM Network, Multi-layer Network, Energy Efficiency

The power consumption of core networks is bound to grow considerably due to increasing traffic volumes. Network reconfiguration adapting resources to the load is a promising countermeasure. However, the benefit of this approach is hard to evaluate realistically since current network equipment does not support dynamic resource adaptation and power-saving features. In this paper, we derive a dynamic resource operation and power model for IP-over-WSON network devices based on static power consumption data from vendors and reasonable assumptions on the achievable scaling behavior. Our model allows to express the dynamic energy consumption as a function of active optical interfaces, line cards, chassis, and the amount of switched IP traffic. We finally apply the model in the evaluation of two network reconfiguration schemes.

Introduction

The continuous exponential growth of the traffic volume is driving an increase in the power consumption of communication networks, which has gained importance due to economic, environmental, and regulatory considerations. While the access segment has traditionally accounted for the largest share of the energy consumed in communication networks, core networks are bound to become predominant due to new energy-efficient broadband access technologies like FTTx [START_REF] Baliga | Energy consumption in optical IP networks[END_REF][START_REF] Hinton | Power consumption and energy efficiency in the Internet[END_REF].

Today, core network resources are operated statically whereas the traffic exhibits significant variations over the day, including night periods with as little as 20 % of the daily peak rate [START_REF] De-Cix | Traffic statistics[END_REF]. Adapting the amount of active, i. e. energyconsuming, resources to the traffic load could therefore significantly reduce power consumption. Since the paradigm of static operation has traditionally guaranteed the reliability of transport networks, research needs to thoroughly investigate both the benefits and the consequences of dynamic resource operation.

A first challenge is to determine the energy savings achievable by this approach. Current core network equipment neither allows the deactivation of components nor features state-of-the-art power-saving techniques (such as frequency scaling found in desktop processors). Hence, its power consumption hardly depends on the traffic load [START_REF] Chabarek | Power awareness in network design and routing[END_REF][START_REF] Hülsermann | Analysis of the energy efficiency in IP over WDM networks with load-adaptive operation[END_REF]. For the static operation of network equipment, researchers have derived power models from measurements [START_REF] Chabarek | Power awareness in network design and routing[END_REF] and product specifications or white papers [START_REF] Van Heddeghem | Power consumption modeling in optical multilayer networks[END_REF]. These models are either intended as a reference for future research [START_REF] Van Heddeghem | Power consumption modeling in optical multilayer networks[END_REF] or applied in power-efficient network planning studies [START_REF] Wang | Energy efficient design for multi-shelf IP over WDM networks[END_REF].

Core network reconfiguration studies base on different power adaptation assumptions. Mostly, components (e. g. optical cross-connects, amplifiers, regenerators [START_REF] Wu | Power-aware routing and wavelength assignment in optical networks[END_REF][START_REF] Silvestri | Wavelength path optimization in optical transport networks for energy saving[END_REF]; router interfaces, line cards [START_REF] Chiaraviglio | Reducing power consumption in backbone networks[END_REF][START_REF] Idzikowski | Dynamic routing at different layers in IP-over-WDM networks -maximizing energy savings[END_REF]) or whole links and nodes [START_REF] Chiaraviglio | Reducing power consumption in backbone networks[END_REF] are switched off. Alternatively, the energy consumption is scaled with active capacity [START_REF] Bathula | Energy efficient architectures for optical networks[END_REF], link bit rate [START_REF] Vasić | Energy-aware traffic engineering[END_REF], or traffic load [START_REF] Puype | Power reduction techniques in multilayer traffic engineering[END_REF]. Restrepo et al. [START_REF] Restrepo | Energy profile aware routing[END_REF] propose load-dependent energy profiles justified by different power-saving techniques for components and also apply them to nodes. Lange et al. [START_REF] Lange | Network element characteristics for traffic load adaptive network operation[END_REF] derive a dynamic power model from a generic network device structure and validate it by measurements of one piece of power-saving enabled equipment. To our knowledge, however, there is no coherent reference model for the dynamic power consumption of current core network equipment assuming state-of-the-art power-scaling techniques. Such a model is highly desirable for network reconfiguration studies.

In this paper, we derive a dynamic resource operation and power model for Internet protocol (IP) over wavelength switched optical network (WSON) networks. We start by decomposing network nodes into their relevant hardware components, for which we obtain static power consumption values from reference models and publicly available manufacturer data. Based on their structure and functions, we identify applicable activation/deactivation patterns and powerscaling techniques and derive the operation state-dependent power consumption. We then aggregate the component behavior into a power model suitable for network-level studies. It features power values for optical circuits and electrically switched traffic. In addition, it takes the resource hierarchy of network nodes into consideration and describes their static power consumption. We finally illustrate the application of our model using reconfiguration schemes from [START_REF] Idzikowski | Dynamic routing at different layers in IP-over-WDM networks -maximizing energy savings[END_REF][START_REF] Feller | Evaluation of centralized solution methods for the dynamic optical bypassing problem[END_REF].

Network Equipment Structure

The two layers of IP-over-WSON networks are reflected in the structure of the network nodes. The nodes typically consist of a router processing IP packets for the upper layer and a wavelength-selective optical circuit switch in the lower layer. Optical circuits interconnect arbitrary IP routers.

IP Routers

Large core routers like the Cisco CRS-3, Alcatel-Lucent (ALU) 7950 XRS, or Juniper T4000 have a modular structure as depicted in Fig. 1. Their basic element is the line card chassis (LCC), which features a passive backplane interconnecting card slots. The chassis is equipped with a number of elements to provide basic functionality. This comprises power supply and cooling units realized in a modular way for failure tolerance and step-wise upgrades. Power supply and cooling Line cards (LC) perform the central role of the router: they terminate optical circuits and switch the traffic on the IP packet level. One can subdivide them according to these functions into port cards (PC) and forwarding engine cards (FE). The latter feature network processors (NP), further application specific integrated circuits (ASIC), and memory to process and store packets. NPs generally have highly parallel architectures 1 . Typically, one part of the memory is used for packet buffering, whereas a copy of the routing table occupies the rest.

PCs provide the connection between optical circuits and the electrical interfaces of FEs. One PC may terminate several circuits. Traditionally, PCs feature ports for pluggable optical transceiver (transmitter and receiver) modules such as SFP 2 , XFP 3 , or CFP 4 . These transceivers (TRX) are so-called short-reach transceivers (SR-TRX) of a limited optical reach (100 m to 100 km); they connect the LCs to transponders (TXP) converting the signal to a given wavelength for long-haul transmission. Alternatively, PCs can directly feature colored interfaces with long-reach transceivers (LR-TRX). This avoids the overhead of signal conversion, but currently restricts the number of interfaces per LC.

Switch fabric cards (SFC) comprising ASICs and buffer memory interconnect LCs over the passive backplane. They are essential for the forwarding of packets between different LCs. The total interconnection capacity between any pair of LCs is distributed over several SFCs, allowing graceful degradation in case of failures. It is likewise possible to interconnect several LCCs using a dedicated 1 Cf. Cisco Quantum Flow Array [START_REF] Ungerman | IP NGN backbone routers for the next decade[END_REF], ALU FP3 [START_REF]New DNA for the evolution of service routing: The FP3 400G network processor[END_REF], EZchip NPS-400 [START_REF] Wheeler | EZchip breaks the NPU mold[END_REF] 2 Small form-factor pluggable, capable of 1 Gbps 3 10 Gigabit small form-factor pluggable 4 C form-factor pluggable, capable of 100 Gbps switch fabric card chassis (FCC), which features further SFCs, in order to create a logical router of higher capacity.

WSON Devices

Functionally, devices of the WSON layer are subdividable into generation/termination of optical signals and their switching and transmission. The former task is performed by TXPs and colored LC interfaces. On the long-reach side, these devices feature lasers for transmission (and as local oscillators in case of coherent detection), photodiodes, digital-to-analog converters (DAC), analog-todigital converters (ADC), and digital signal processing (DSP) ASICs. In addition to forward error correction (FEC), the DSP performs the complex task of compensating for optical impairments in the case of high bit-rate channels.

In WSON, optical switching nodes transfer wavelength-division multiplex (WDM) signals between fibers connecting to neighbor nodes and from/to local TXPs/TRXs. There exists a variety of configurations differing in functionality and complexity. We assume the setting depicted in the lower part of Fig. 2. It is colorless and directionless, meaning that any incoming WDM signal is switchable to any output fiber (as long as there is only one fiber pair to each neighbor node). Each wavelength may only be used once in the node. Key components are optical splitters relaying channels to neighbor nodes and wavelength selective switches (WSS), which are able to select any wavelength from either the local ring or the incoming fiber. In addition, WSSs act as multiplexer (mux) and demultiplexer (demux) to insert and drop locally terminated channels.

Optical (i. e. analog) signal amplification is not only needed at the input and output of the optical node, but also along the fibers. Typically, an optical line amplifier (OLA) is placed every 80 km. Introducing noise, this in turn limits the distance without electrical (i. e. digital) signal regeneration (essentially by two back-to-back TXPs) to between 800 km and 4000 km.

Like in the IP layer, the optical components present in nodes are organized in shelves providing power supply, cooling, and control (cf. Fig. 2, top). Such shelf systems are e. g. ALU 1830 Photonic Service Switch, ADVA FSP 3000 or Cisco ONS 15454 Multiservice Transport Platform.

Static Power Consumption Values

We derive our power model from static (maximum) power values for Cisco's CRS-3, ONS 15454 MSTP, and their respective components, as well as a stand-alone EDFA by Finisar. We mainly refer to Cisco equipment since to our knowledge, Cisco is the only vendor to publicly provide power values of individual components for both core router and WDM equipment. This also makes our work comparable to that of other researchers who use the same data for similar reasons [START_REF] Van Heddeghem | Power consumption modeling in optical multilayer networks[END_REF][START_REF] Wang | Energy efficient design for multi-shelf IP over WDM networks[END_REF][START_REF] Rizzelli | Energy efficient traffic-aware design of on-off multi-layer translucent optical networks[END_REF]. Table 1 lists the static power values (along with variables referring to them in Sect. 4.2). They are largely identical to the ones in the Powerlib5 . In the CRS-3, the components are combined as follows: One LCC houses one to eight power supply modules, which can reach an efficiency of about 95 % [START_REF]Cisco: CRS Carrier Routing System Multishelf System Description[END_REF]. In addition, there are two fan trays and two CC cards (also acting as RE). The switch fabric is realized by an output-buffered Beneš network distributed on 8 parallel SFCs. A multi-chassis configuration requires an FCC with a set of control modules and eight special SFCs and optical interface modules (OIM) per group of three LCCs [START_REF] Wang | Energy efficient design for multi-shelf IP over WDM networks[END_REF]. The LCCs then use a different type SFCs with a similar power consumption. We use the most powerful type of FE (or Modular Services Card ) capable of 140 Gbps, which allows for PCs with a maximum of 1x 100G, 3x 40G or 14x 10G ports at full line rate. On the optical layer, we consider TXPs matching these performance values. While the 40G TXP has an integrated SR-interface, the 100G TXP and 10G TXP need additional SR-TRXs.

For the ONS 15454 MSTP version with 100G-capable backplane, Cisco lists a power consumption of 284 W for shelf, cooling, and controller card [START_REF]Cisco: ONS 15454 Hardware Installation Guide -Appendix A[END_REF]. We estimate the power consumption of the larger 12-port version, for which no such reference is available, to 260 W, since both cooling and CC are slightly less power-hungry for this version (although exact numbers vary between references 6 ). Contrary to the specification, we assume that the 12-port version can hold the 100G TXP as long as the backplane is not used. Unlike the stand-alone OLA, the OLA modules for the shelves are unidirectional. Consequently, two of them are deployed for each connection to another router. The WSS is Cisco's 80-channel WXC, which additionally comprises a passive beam splitter.

Dynamic Resource Operation and Power Model

We derive a dynamic model for the momentary power consumption of a hypothetical core network node with extended power saving features. For the application in network-level studies, we limit the scope of power scaling to adapting processing capacity to the amount of packet-switched traffic and modifying optical circuits (along with the hierarchically required resources like LCs and LCCs). The according scaling mechanisms operate at different time scales: To follow fast fluctuations of the packet rate, processing capacity needs adaptation in the order of microseconds. In contrast, establishing or tearing down an optical circuit takes in the order of several minutes due to transient effects in OLAs. We assume that this latter time scale enables the (de)activation of any electronic component.

In the following, we discuss the power scaling possibilities of the node's components. We then describe the resulting model.

Dynamic Operation and Power Scaling Assumptions

Power Supply: We expect the CRS power supply modules to behave like the most efficient Cisco models, which reach an efficiency of at least η = 90 % in a load range of 25 % to 90 % [36]. Since chassis are operable with a varying number of such modules, we assume that we can switch modules off to stay in the 90 % efficiency region. We accordingly derive the gross power consumption by increasing all power values by a factor of 1/η ≈ 1.11 .

Cooling:

The affinity laws relate the power consumption P i of fans at operation point i to their rotational speed N i by P 1 /P 2 = (N 1 /N 2 )

3 . The normal operation range of CRS fans is 3300 RPM to 5150 RPM, with the maximum rated at 6700 RPM [START_REF]Cisco: CRS Carrier Routing System 16-Slot Line Card Chassis System Description[END_REF]. We obtain a net minimum power consumption of (3300/6700) 3 ≈ 12 % of the total fan tray power. Accounting for driver overhead, we estimate the static fraction at 20 % and let the remainder scale linearly with the number of active LCs, since these produce the largest share of heat.

Chassis Controller Cards: Being a small general-purpose server system, the CC can readily benefit from power saving features like dynamic voltage and frequency scaling (DVFS) or power-efficient memory [START_REF] Malladi | Towards energy-proportional datacenter memory with mobile DRAM[END_REF]. We do however not model the control workload and consequently consider that the CC consume constant (maximum) power.

Switch Fabric:

The interconnection structure prohibits the scaling of SFCs along with LCs, and the signaling time overhead for SFC (de)activation impedes an adaptation to the switched IP traffic. We therefore assume all SFCs to be active when their LCC is so, and we disregard power scaling options of ASICs and memory on SFCs. For multi-chassis routers, we allow active SFCs and OIMs in the FCC to scale with LCCs in accordance with possible static configurations (i. e. we switch blocks of eight SFCs and OIMs per group of three LCCs).

Forwarding Engine Cards: ASICs and NPs account for 48 % of the power budget of a LC, memory for 19 %; the remaining 33 % is spent on power conversion, control and auxiliary logic [START_REF] Epps | System power challenges[END_REF]. We assume the latter part to be static. The same applies to 9 % (out of the 19 %) of memory power consumption for the forwarding information base [40]. The remainder (10 % of the LC consumption) is for buffer memory, which is presumably dimensioned for the 140 Gbps capacity of the FE following the bandwidth-delay product (BDP) rule. We let the active buffer memory scale according to the BDP with the capacity of the active circuits terminated by the LC. Neglecting the residual power consumption in deep sleep state and the discrete nature of switchable memory units [40], we assume the buffer's power consumption to scale linearly with the active port capacity.

Recent NP designs support power saving by switching off unused components, e. g. cores [START_REF]New DNA for the evolution of service routing: The FP3 400G network processor[END_REF]41,[START_REF] Ungerman | Anatomy of Internet routers[END_REF]. In theory, deactivating and applying DVFS to NP cores alone can save more than 60 % in low traffic situations [START_REF] Mandviwalla | Energy-efficient scheme for multiprocessor-based router linecards[END_REF]. We therefore assume that 70 % of the power consumption of ASICs and NPs dynamically scales with processed IP traffic, while 30 % is static. In sum, we assign 33 % + 9 % + (30 % • 48 %) = 56.4 % statically to an active LC; we let 10 % scale with the active port capacity and 70 % • 48 % = 33.6 % with processed traffic.

Port Cards, Transceivers, Transponders: Like the latest hardware generation [START_REF] Lange | Network element characteristics for traffic load adaptive network operation[END_REF]41], we allow the dynamic deactivation of unused LC ports. We assume that a (multi-port) PC is active along with the FE as long as one of its ports is so. TRXs and TXPs are switched on and off with the respective circuits. We disregard more fine-grained power scaling proposals for TXP ASICs [START_REF] Le Rouzic | TREND towards more energy-efficient optical networks[END_REF], but we do assume that the transmit and receive parts of a TXP may be activated separately 7 . In case of a PC with multiple colored interfaces, we distribute the total power consumption of the PC to the ports and let it scale with the circuits.

Optical Node: We consider the power consumption for cooling, power supply, and control of an optics shelf as static, since it is much less than the consumption of the respective LCC modules. The same applies to OLAs and WSSs. We do however allow the deactivation of TXP-hosting shelves when all TXPs are switched off.

Resulting Power Model

For the mathematical model, we use the following conventions: Capital letters without indexes represent equipment-specific constants. C α denotes capacity in Gbps, P α maximum power consumption and N α the maximum installable quantity of component α. The set of all possible values for P α is denoted by P α . Indexed variables denote a specific component in a specific node. Variables of the type n β represent dimensioning parameters describing the configuration of one node. Small letters indicate model parameters characterizing the dynamic configuration and load situation.

A router may consist of a maximum of N LCC = 9 LCCs; the actual number is n LCC ∈ {1..N LCC }. If the router has more than one LCC, a FCC is needed. In this case the first factor of (1) is nonzero, and so is P FCC,stat .

A LCC is always equipped with N SFC = 8 SFCs, N CC = 2 CC cards and N CU = 2 cooling units. We consider 20 % of the cooling power consumption as static, resulting in the total static LCC consumption in [START_REF] Hinton | Power consumption and energy efficiency in the Internet[END_REF]. The remaining 80 % of the cooling units gives the dynamic power consumption of LCC i with x i active out of n LC,i ∈ {1..N LC } installed LCs according to (3). One LCC houses at most N LC = 16 LCs. An active LC statically consumes 56.4% of P FE and the total of the respective port card out of P PC . The power cost of the j-th LC in LCC i is thus given by (4). The power consumption per active port has two components: The packet buffers of the FE and the respective TRX P TRX,ij ∈ P TRX . For colored LC interfaces, P TRX,ij is the consumption of the PC over the number of ports and P PC,ij = 0. The buffer's consumption (10 % of P FE ) is scaled to the capacity of one port C P,ij (C FE = 140 Gbps is the total capacity of the FE). The dynamic LC power share is obtained by multiplying these contributions by the number of active ports y ij , (5). Equation ( 6) represents the traffic-dependent power consumption of a LC. The variable z ij indicates the traffic demand in Gbps at LC j in LCC i.

In the optical layer, we assume a ring configuration according to Fig. 2. Each of the n d links to a neighbor node requires one WSS module and two amplifiers. The router is connected through two additional WSS modules. WSS modules, amplifiers and TXPs each use one of 12 available slots in an optics shelf. This adds up to the static power of optical components P Opt,stat in [START_REF] Wang | Energy efficient design for multi-shelf IP over WDM networks[END_REF], where n λ is the maximum number of wavelengths needed on one link, N λ = 80 the maximum number of wavelengths per fiber and P WSS , P Amp and P OS are the power consumptions of a WSS module, OLA module and optics shelf respectively. n TXP is the number of installed TXPs.

P FCC,stat = n LCC -1 N LCC n LCC 3 • 8(P OIM + P S2SFC ) + P SCC . (1)
P LCC,stat = N SFC • P SFC + N CC • P CC + N CU • P CU • 20% . (2)
P LCC,dyn,i (x i ) = x i N LC • N CU • P CU • 80% . (3)
P LC,stat,ij = P FE • 56.4% + P PC,ij .

P LC,dyn,ij (y ij ) = P FE • 10% C FE • C P,ij + P TRX,ij • y ij . (4)
P LC,traff,ij (z ij ) = 48% • 70% • P FE C FE • z ij . (5)
P Opt,stat = n λ N λ • n d • (P WSS + 2 • P Amp ) + 2 • P WSS + n λ N λ • (n d • 3 + 2) + n TXP • 1 12 • P OS . (6)
Equation ( 8) finally gives the dynamic consumption P Opt,dyn of the TXPs connected to LC j in LCC i.

P Opt,dyn (y ij ) = nLCC i=1 nLC,i j=1 P TXP,ij • y ij . (8)

Application Example

We illustrate the application of the dynamic power model by evaluating the power savings achieved by different degrees of network reconfiguration assuming two different node configurations.

Scenario

Node Configuration. We assume WDM channels of 40 Gbps, which are terminated either by colored LC interfaces (case A) or by TXPs in a WDM shelf connected to LCs via short-reach optics (case B ). While each LC can only feature one interface in case A, we serve up to three 40 Gbps channels with one LC in case B. In both cases, add/drop traffic is handled by 10 Gbps short-reach interfaces on dedicated tributary LCs (with up to 14 such interfaces). Unlike resources on the core network side, all tributary interfaces are constantly active. Table 2 gives the numerical power values. The port comprises TXP (and SR-TRX in case B) as well as the dynamic line card power share. Values for the optical equipment are used in [START_REF] Wang | Energy efficient design for multi-shelf IP over WDM networks[END_REF] according to node dimensioning.

Network and Traffic. We present results for the 22-node Géant reference network (available from SNDLib [START_REF] Orlowski | SNDlib 1.0-Survivable Network Design Library[END_REF]) using ten days out of demand traces collected in 2005 [START_REF] Uhlig | Providing public intradomain traffic matrices to the research community[END_REF]. Assuming similar statistical properties in these ten days, we give 95 % confidence intervals for the metrics. To vary the network load, we linearly scale the demand traces and quantify this scaling based on a peak demand matrix containing the peak values of each node-to-node demand trace. We scale the traces such that the average of the values in the peak demand matrix ranges between 2 Gbps and 160 Gbps, corresponding to a total peak demand (sum over all values in the matrix) of 924 Gbps to 73.9 Tbps.

Resource Adaptation. We consider three operation schemes and evaluate the power consumption every 15 minutes: (i) In a static network configuration (regarding virtual topology and demand routes optimized for the peak demand matrix), we let active resources scale with the load. This corresponds to FUFL in [START_REF] Idzikowski | Dynamic routing at different layers in IP-over-WDM networks -maximizing energy savings[END_REF]. (ii) We reconfigure the virtual topology by periodically applying the centralized dynamic optical bypassing (CDOB) scheme according to [START_REF] Feller | Evaluation of centralized solution methods for the dynamic optical bypassing problem[END_REF], using the actual power consumption as cost function with the simulated annealingbased solution method. (iii) The static operation of all resources dimensioned for the peak demand matrix serves as baseline.

Results

Fig. 3 plots the average power consumption of all devices in the network over the average peak demand for the different configuration cases and adaptation schemes. As one would expect, the power consumption increases with the load in all cases. We further observe that the power consumption is systematically higher for case A compared to the same adaptation scheme in case B: the savings due to the higher port density per LC and per LCC overcompensate the cost for additional short-reach optics. Energy savings by resource adaptation in an otherwise static network configuration range between 20 % for low average load and 40 % for high load in case A. The increasing benefits are explained by a higher number of parallel circuits allowing deactivation. CDOB likewise requires a certain amount of traffic to be effective and yields 10 additional percentage points of savings over the simple dynamic resource operation at maximum load. In case B, the achievable savings only range between 20 % and 33 % (resp. 44 % for CDOB), albeit at an altogether lower power consumption level. This effect is explained by the resource hierarchy: unlike in case A, LCs may need to remain active when only a fraction of their ports is used.

Conclusion

In this paper, we derived a dynamic power model for IP-over-WSON network equipment assuming the presence of state-of-the-art power-saving techniques in current network devices. Based on an in-depth discussion of the power scaling behavior of the components of the devices, we express the dynamic power consumption of a network node as a function of the optical circuits it terminates and the pieces of the hardware hierarchy (line cards, racks) required for this. In addition, a smaller share of power scales with the amount of electrically processed traffic.

Lacking reliable data on power scaling options for optical switching equipment and amplifiers, we assume that the power consumption of these devices is independent of the load. While the model is open to improvement in this respect, the impact of this limitation is small given the predominance of the power consumption of IP layer equipment and transceivers.

By applying our power model to different network resource adaptation schemes in an example scenario, we found that dynamic resource operation can reduce the total power consumption of the network by 20 % to 50 %. However, generalizing these figures requires much caution since they strongly depend on the assumed resource dimensioning in the static reference case.

Future work could extend the dynamic power model in order to take new devices and trends into account. E. g., the power consumption of rate-adaptive transponders could be modeled. Likewise, different optical node variants could be included. Besides, the model awaits application in research on network reconfiguration.

Fig. 3 .

 3 Fig. 3. Network-wide power consumption

Table 1 .

 1 Static power values per component. Symbols are explained in section 4.

	Component type	Power Consumption	Number installable Source
	FCC control per chassis	1068 W /η := PSCC	1 for 9 LCC	[21]
	Switch Fabric Card for FCC	229 W /η := PS2SFC	8 for 3 LCC	[21]
	Optical Interface Module	166 W /η := POIM	8 for 3 LCC	[21]
	Chassis Control	275 W /η := PCC	2 per LCC	[22]
	Fan Tray	344 W /η := PCU	2 per LCC	[6]
	Switch Fabric Card for LCC	206 W /η := PSFC	8 per LCC	[6]
	140G Forwarding Engine	446 W /η := PFE	1 to 16 per LCC	[6]
	1x 100G LR-TRX	180 W /η ∈ P TRX	1 per FE	[23]
	2x 40G LR-TRX	150 W /(2 • η) ∈ P TRX 1 per FE	[6]
	4x 10G LR-TRX	150 W /(4 • η) ∈ P TRX 1 per FE	[6]
	1x 100G SR-port card	150 W /η ∈ P PC	1 per FE	[6]
	2x 40G SR-port card	185 W /η ∈ P PC	1 per FE	[24]
	4x 40G SR-port card	185 W /η ∈ P PC	1 per FE	[25]
	14x 10G SR-port card	150 W /η ∈ P PC	1 per FE	[6]
	20x 10G SR-port card	150 W /η ∈ P PC	1 per FE	[6]
	1x 100G SR-TRX (CFP)	12 W /η ∈ P TRX	1 per 100G PC	[26]
	1x 100G SR-TRX (CXP)	6 W /η ∈ P TRX	1 per 100G TXP	[27]
	1x 40G SR-TRX	8 W /η ∈ P TRX	1 to 4 per 40G PC [28]
	1x 10G SR-TRX	3.5 W /η ∈ P TRX	1 to 14 per 10G PC [27]
	1x 100G TXP (excl. SR-TRX) 133 W /η ∈ P TXP	6 per 100G-Shelf	[29]
	1x 40G TXP (incl. SR-TRX)	129 W /η ∈ P TXP	6 per shelf	[30]
	1x 10G TXP (excl. SR-TRX)	50 W /η ∈ P TXP	12 per shelf	[6]
	OLA standalone	50 W /η := POLA	1 every 80 km	[31]
	OLA card	40 W /η := PAmp	2 on every edge	[32]
	WSS card	20 W /η := PWSS	1 on every edge	[33],[6]
	12-Port Optics Shelf	260 W /η := POS	-	[34],[33]
	6-Port Optics Shelf (100G)	284 W	-	[34]

Table 2 .

 2 Power values (in Watts)

	Component	Contribution	Case A Case B
	FCC	static if >1 LCC	1124	1124
	SFC in FCC	per 3 LCC	3326	3326
	LCC	if ≥ 1 LC on	2336	2336
	LC	if ≥ 1 port on	286	471
	Port	per circuit	163	150
	Tributary LC static	436	436
	Tributary port static	3.5	3.5
	IP processing per Gbps	1.07	1.07
	Optics shelf	per 12 units	260	260
	WSS+2Ampl. static	100	100
	2 WSS	static	40	40

Cf. http://powerlib.intec.ugent.be and[START_REF] Van Heddeghem | Power consumption modeling in optical multilayer networks[END_REF]

Cf. tables A-1, A-4 and A-5 from[START_REF]Cisco: ONS 15454 Hardware Installation Guide -Appendix A[END_REF] and table A-1 from[START_REF]Cisco: ONS 15454 DWDM Reference Manual -Appendix A[END_REF]

The power distribution between the parts is of minor relevance since one of each is needed for one circuit.

Acknowledgments. The work presented in this paper was partly funded within the SASER project SSEN by the German Bundesministerium für Bildung und Forschung under contract No. 16BP12202


Irem Nizamoglu
email: inizamoglu@ku.edu.tr

Sinem Coleri Ergen

Oznur Ozkasap
email: oozkasap@ku.edu.tr

EpiDOL: Epidemic Density Adaptive Data Dissemination Exploiting Opposite Lane in VANETs

Vehicular ad-hoc networks (VANETs) aim to increase the safety of passengers by making information available beyond the driver's knowledge. The challenging properties of VANETs such as their dynamic behavior and intermittently connected feature need to be considered when designing a reliable communication protocol in a VANET. In this study, we propose an epidemic and density adaptive protocol for data dissemination in vehicular networks, namely EpiDOL, which utilizes the opposite lane capacity with novel probability functions. We evaluate the performance in terms of end-to-end delay, throughput, overhead and usage ratio of the opposite lane under different vehicular traffic densities via realistic simulations based on SUMO traces in ns-3 simulator. We found out that EpiDOL achieves more than 90% throughput in low densities, and without any additional load to the network 75% throughput in high densities. In terms of throughput EpiDOL outperforms the Edge-Aware and DV-CAST protocols 10% and 40% respectively.

Introduction

For improving safety of the roads, vehicular ad-hoc networks (VANETs) has become popular both in industry and academia. Efficient usage of vehicular networks has significant potential considering the fact that the amount of traffic accidents is massive. For instance, every year just in the United States almost six million traffic accidents, ten thousands of deaths and millions of injuries occur [START_REF] Biswas | Vehicle-to-vehicle wireless communication protocols for enhancing highway traffic safety[END_REF]. Certainly, VANETs are expected to significantly improve the safety of our transportation systems by making information available beyond the drivers' knowledge. In ad-hoc networks, routing protocols are crucial to maintain reliable and efficient communication. Despite this importance, most of the protocols offered for VANET are reinterpretations of the well known mobile ad-hoc network (MANET) routing protocols. However, VANETs behave in fundamentally different ways than the models that predominate in MANET researches. Unlike traditional MANETs, they have high but more predictable mobility models with rapid changes due to high speed and frequent fragmentation in network topology [START_REF] Li | Routing in vehicular ad hoc networks: A survey[END_REF]. Geographic position information is available through Global Positioning System (GPS) with no power or hard delay constraints. Therefore, communication methods developed for VANETs must consider various dimensions including delay and reliability requirements, protocol specifications, vehicle mobility, topology characteristics and physical constraints. Thus, instead of using existing protocols proposed for MANETs, design of protocols specifically for VANETs to mitigate its disadvantages and utilize GPS information is needed.

In this study, we propose and develop a novel data dissemination protocol for VANETs. In contrast to prior studies, our protocol EpiDOL uses epidemic routing by using the advantage of opposite lane as relaying with novel probability functions among vehicles. Epidemic technique introduces intelligence into data dissemination by reducing contentions and collisions while not requiring infrastructure support. Our probability functions are simple but effective in providing adaptivity to density. Therefore, the comparison of our approach with state of the art protocols DV-CAST [START_REF] Nekovee | Epidemic algorithms for reliable and efficient information dissemination in vehicular[END_REF] and Edge-Aware [START_REF] Tonguz | Dv-cast: A distributed vehicular broadcast protocol for vehicular ad hoc networks[END_REF] in realistic traces, proved the efficiency of EpiDOL in terms of lower delay, higher throughput rates and better utilization of opposite lane relaying. The novelties of this work as following.

-We propose and develop a density adaptive epidemic data dissemination protocol, EpiDOL which uses only limited network information and control flags that indicate packet dissemination direction and vehicles' movement direction.

-In EpiDOL, we propose a new probabilistic approach to utilize opposite lane nodes as relay node to solve disconnected networks problem.

The original contribution is to propose probabilistic density adaptive epidemic data dissemination protocol, EpiDOL. Also, we analyze the dependence of optimal parameters of epidemic routing on the density and the direction of vehicles.

In addition, we evaluate the performance of EpiDOL compared with the current routing algorithms with simulations that use realistic traffic traces. The rest of this paper is organized as follows. Section 2 describes related works. Section 3 discusses the system model. Section 4 describes the details of EpiDOL with performance metrics that we used during the simulations. Section 5 provides scenarios used in simulations and performance criteria. Section 6 presents simulation results and Section 7 concludes the paper.

Related Works

Distributing Internet on roads [5] or using mobile phones and in-car embedded devices for collecting and processing data [6] are possible applications of VANET. To realize these applications, we have to disseminate the data throughout a network. While thinking ad-hoc networks, the simplest and the most common way of data dissemination is flooding. However, as a result of the redundant broadcasts there may be contentions and collisions in the shared wireless medium. VANET routing protocols mainly deal with two problems, broadcast storm and disconnected networks [START_REF] Tonguz | Dv-cast: A distributed vehicular broadcast protocol for vehicular ad hoc networks[END_REF]. When high number of nodes start to disseminate their packets at the same time, it is highly probable that the collisions will occur. The loss of data packets due to these collisions are defined as broadcast storm problem. [START_REF] Tonguz | Broadcasting in vanet[END_REF] and [START_REF] Wisitpongphan | Broadcast storm mitigation techniques in vehicular ad hoc networks[END_REF] try to solve this problem in MAC layer level by including some new ideas such as disseminating packets with probability functions. [START_REF] Schwartz | Fair and adaptive data dissemination for traffic information systems[END_REF] develops a scheme to distribute packets fairly to the network by using the local knowledge. We have further improved these approaches by introducing probability functions adaptive to density. EpiDOL uses epidemic approach with different probability functions succeeded to decrease the packet loss and overhead while increasing the throughput significantly.

According to [START_REF] Blum | Challenges of intervehicle ad hoc networks[END_REF] other challenges in VANET i.e. vehicle movements and driver's behavior cause rapid topology changes and frequent fragmentation on the network. Possible link breakages are predicted using the velocity of the nodes in [START_REF] Taleb | An efficient vehicleheading based routing protocol for vanet networks[END_REF] and [START_REF] Taleb | A stable routing protocol to support its services in vanet networks[END_REF]. We deal these problems by using periodically updated neighborhood info. Additionally the dynamic behaviors of the vehicles and the sparsely connected networks introduce new problems. However it is already shown that Gossip-based (Epidemic) protocols are effective to solve these problems and provide reliable and efficient communication [START_REF] Costa | When cars start gossiping[END_REF].

Reactive and proactive protocols have different behaviors under different traffic regimes [START_REF] Li | Routing in vehicular ad hoc networks: A survey[END_REF], they should be robust to different density levels. [START_REF] Meireles | Dazl: Density-aware zone-based packet forwarding in vehicular networks[END_REF] presents a zone-based forwarding scheme to deal with density problems. At low density networks the disconnected network problem is a severe issue. It can be defined as the case when there are not sufficiently enough nodes for data transmission in the network. It is proposed that by choosing the best packet structure this problem can be solved [START_REF] Taleb | An efficient vehicleheading based routing protocol for vanet networks[END_REF]. To deal with this problem, the proposal and successful implementation of the intelligible use of the vehicles in the opposite lane for relaying packets between disconnected networks in the original lane is another novel contribution of this work.

[15] and [START_REF] Zuo | Performance evaluation of routing protocol in vanet with vehicle-node density[END_REF] reinterpret well-known protocols AODV, GPSR and OLSR instead of proposing specialized approaches for VANET. Addition to this different techniques, Edge-aware Epidemic Protocol [START_REF] Nekovee | Epidemic algorithms for reliable and efficient information dissemination in vehicular[END_REF] is the most relevant study to this work. Edge-aware detects edge nodes and assigns high probabilities to these nodes. However, EpiDOL extends this approach with its opposite lane usage and simple but effective probabilistic forwarding technique.

System Model

We consider an ad-hoc network with randomly distributed vehicles on a multilane bidirectional highway. Each vehicle is equipped with GPS and has the communication capability with 802.11p protocol [START_REF]Ieee standard for information technology-local and metropolitan area networksspecific requirements-part 11: Wireless lan medium access control (mac) and physical layer (phy) specifications amendment 6: Wireless access in vehicular environments[END_REF]. We assumed that the nodes are only interested in the packets that are generated by nodes that lead them within less than a certain region of interest (ROI). All packets are generated by a leading node with the same priority and the same dissemination distance. Any packet can be lost due to collisions, however, if a packet is received successfully, then there are no bit errors that lead to the misinformation of the node.

Since there is no central control or clustering mechanism, all nodes in the network act independently. They are only aware of their neighbors' locations and directions by periodical updates which will be discussed in Section 4. It is assumed that any further information about the network topology or density are not available to the vehicles.

Algorithm Description

In this section, we describe principles of EpiDOL with its system architecture and probability functions.

System Architecture

In our approach, we aim to create an intelligent packet dissemination system by using flags on application layer. We use two binary flags: of shows the actual dissemination direction of the data packet, and df shows the vehicles riding direction with respect to the direction of the source node. With the help of the information that we gathered from these flags, the opposite lane is used effectively to provide the data connectivity and propagation.

Unnecessary packet dissemination is obstructed by using density adaptive probability functions p same , p opp and p sameT oOpp which are the probabilities of forwarding packets in the same direction, in the opposite direction and transmitting packets from original direction to the opposite direction, respectively. This approach solves the broadcast storm problem by decreasing the collision rate. We have information about neighbors of each node by sending hello packets periodically. The neighbor number provides a simple but effective density adaptation in the algorithm.

In our system, there exist two types of packets. One of them is the periodic hello packet that includes source id, x-y coordinates of the source and its traveling direction. When hello packets are received, the receiver node creates its neighborhood list which includes information about its neighbors. Hello packets are like control packets. By using this method, we can easily manipulate the number of neighbors and their locations. The second type is data packet which includes the senders' id, x-y coordinates, the packet dissemination distance, the direction flag and the original flag. The dissemination distance shows how far the data packet should be propagated.

Algorithm 1 shows the decision phases of flag values in EpiDOL. While the vehicle is moving in the packets' dissemination direction (Line 1-2), if there are any same directional neighbors (Line 3), it means that packet will propagate in the original side, of is 1 (Line 4). Additionally, for providing the continuity of the packet dissemination, EpiDOL sends this packet to the opposite directional vehicles (Line 5) by setting the of and df to 0 (Line 6). Another case is when the value of df is 0 (Line 9-10). This shows that vehicle is moving in the opposite direction of the packets' dissemination. If there are any same directional neighbors (Line 11), packet is sending to them with the same flag values (Line 12). If there are any opposite directional neighbors (Line 13), which are actually in the original side, of value is set to 1 (Line 14), showing that the packet returns its original directional side.

Probability Functions

The decision of forwarding a packet or not is taken by a probabilistic manner at each node independently. Prior to the each packet transmission, a probability that estimates the necessity of the transmission of a packet from a particular node is calculated with the help of the number of total neighbors. We assumed that the more the neighborhood number is, the higher the chance of nodes receiving the data packet. With this assumption in mind the most trivial probability function is p = 1/N , where N is the number of neighbors, however it can easily be proven that this function will not perform good in dense networks. Within a neighborhood with N nodes, the probability of a packet which is not transmitted by a particular node is p c = 1 -1/N . In homogeneously distributed dense network, we can safely assume that there are no clustering, so each node will approximately have the same number of neighbors. Since each node decides independently with the assumption of a packet is received by N nodes the probability of a packet not forwarded by any nodes is;

Let N = #of N eighbors. Then,

p c N = 1 - 1 N N (1)
If we take the limit of this probability as N goes to infinity, to see the probability of a packet being not forwarded by any nodes in a dense network,

lim

N →∞ p c N = lim N →∞ 1 - 1 N N = lim N →∞ 1 e - 1 2eN - 5 24eN 2 + ... = 1 e ≈ 0.37
This shows that, in dense networks since p is so small, the dissemination of the packet will be stopped with approximately 0.37 probability. In real life, due to collisions in a dense network, the number of nodes that receive a packet is much less than N. Consequently the probability of a packet not forwarded by any nodes is even higher than 0.37. For avoiding these situations, we multiply our value with α parameter. So we choose p = α N which decreases the p c N to 1 e α for large N . Note that the case of p > 1 is treated as if p = 1. Both p same and p opp are calculated with this function. However, according to our simulations, we detected that the best α values are different for propagating packets in the original and in the opposite directions. Thereon we use 2 different α values; α same and α opposite which are optimized by evaluating the different α values on various scenarios. The decision of using opposite lane nodes as relay nodes not only depends on the number of neighbors but also depends on the spatial distribution of the nodes around. The extreme case is the vehicles at the rear end of a cluster. Basically they will have large number of neighbors due to number of vehicles that lead them, however these neighbors are not helpful for propagating packets to backward direction. With this intuition we proposed the following Algorithm 2 to decide on usage of opposite lane nodes. Therefore, the corresponding p sameT oOpp is equivalent to P {#BackwardN eighbors ≤ backwardV alue}. This function helps to solve the disconnected networks problem by continuing the packet dissemination using opposite sided vehicles as relaying nodes. Backward functions use Algorithm 2. In this function, only vehicles in the rear end of a connected cluster are sending packets to the opposite lane.

Implementation

In our simulations, we use SUMO [START_REF]Sumo -simulation of urban mobility[END_REF] for generating realistic low and high density traces. The simulation environment is ns-3 [START_REF]Network simulator[END_REF]. According to [START_REF] Zhang | Routing protocols for vehicular ad hoc networks in rural areas[END_REF], the typical transmission range is 400 meters. We consider a network with randomly distributed vehicles over a 6-lane bidirectional highway. Our region of interest (ROI) has a length of 5km. Since we assumed that there are not any intersections in our ROI, neither death nor birth of a node are allowed. To imitate the dynamic behavior of a highway, the speeds of the vehicles are uniformly distributed from 80km/h to 120km/h.

Performance Metrics

EpiDOL is designed for merging epidemic approach with highly mobile ad-hoc networks. In order to evaluate its reliability and efficiency, we use the following performance metrics versus node density which is vehicles/square meter.

Moreover, these metrics also enable an objective comparison of our proposed algorithm with other approaches.

-End-to-End Delay: Time taken for packet transmission from source to nodes which are in the range of dissemination distance. For each packet received by every node, it is given by; End-to-End Delay = t receive -t f irstSending . -Throughput: This parameter shows the rate of successfully received packets by all nodes which are in the dissemination distance. Calculation is as follows: Throughput = #received packets for each node # all transmitted packets × 100. -Opposite Lane: This parameter measures how many times opposite lane nodes resend the packets that are taken from the original side. Calculation is as follows: Opposite Lane = #packets sent by opposite directional nodes # all packets × 100. -Overhead: The number of duplicate packets received during the simulation.

The overhead is simply equal to the number of received duplicate packets at each node.

Optimization of α same

For optimization of the α same value, we generated SUMO traces which include 10 to 500 vehicles ride in the same direction. Changing α values from 3 to the 30, we produced 3D graphs in Fig. 1. According to these results, α same is chosen as 15, since 90% throughput is achieved while the end-to-end delay is less than 0.06s and the overhead is lower than 0.07. α same being equal to 15 ensures if we have less than 15 vehicles within the coverage area, all nodes will try to forward packets. This is desirable in a sparse network, since at low density our main concern is the survival of the packet rather than the packet collisions. Besides, as the neighbor numbers increase and p same decreases the expected number of retransmissions in a certain area will remain around 15. As seen in Fig. 1b, after a certain node density the overhead does not increase at all for α same = 15. This supports our claim about our p same being sensitive to node density in the network. It is obvious that the performance can be increased with the perfect knowledge of the network density. However, due to excessive control packets, acquiring this information will increase the network overhead significantly, that might even decrease the overall throughput.

Optimization of α opposite

For optimization of the α opposite value, we generated SUMO traces which include 10 to 300 vehicles in the opposite direction and 60 vehicles in the original (same) direction. For α values from 3 to 30, the results are shown in Fig. 2. According to these results, α opposite is chosen as 21, since 97% throughput is achieved while end-to-end delay is less than 0.1s and overhead is lower than 0.1. The optimal α opposite being more than α same is reasonable since we need more persistent transmissions to carry packets in between disconnected networks. Also Fig. 2a shows that even in really low densities, the utilization of the nodes in the opposite direction can double the throughput with α opposite equals to 21. This proves that the regardless of the number of nodes in the opposite direction, we should use them as relay nodes.

Optimization of backwardV alue

backwardV alue is used as a threshold in calculation of the p sameT oOpp which decides whether sending packets from the original side to the opposite side or not. Consequently, choosing the best backwardV alue is crucial for the performance of the algorithm. To reason about and select the optimal backwardV alue, we simulated networks with different densities using backwardV alue's from 0 to 17. Fig. 3 compares the throughput rates with different backwardV alue's. However we should focus the part where node density is less than 3 × 10 -3 . At higher densities, opposite lane usage does not really improve the throughput, since we do not observe disconnected networks problem anymore, consequently all backwardV alue's converge to same throughput levels. However, the average overhead for densities higher than 3 × 10 -3 does not significantly differ for different backwardV alue's as shown in Fig. 4. At high density conditions, the necessity of using opposite lane decreases. According to our function, the number of eligible vehicles which send packets to opposite lane also decreases while the density increases. In summary, by using the appropriate backwardV alue we can double the throughput in low densities in return of higher overhead, however our main concern is to maintain connectivity rather than overhead in low densities. On top of this for higher densities, even though backward function can not improve the throughput, it also does not significantly increase the overhead which is the limiting factor. This proves the density adaptivity of our approach. To decide the optimal value of backwardV alue, we have to consider Fig. 3 and Fig. 4 simultaneously. According to Fig. 3, to achieve 90% throughput in lower densities, backwardV alue should be higher than 9. However, for backwardV alue's greater than 9, the throughput does not increase at all. Furthermore, considering overhead values of Fig. 4 for several different vehicle densities, the optimum backwardV alue is determined as 11. As shown in Fig. 5, an evaluation of this backwardV alue along with two others has been performed and these results indicate the density adaptive nature of opposite lane usage ratio with the probability function making use of number of backward neighbors.

Compared Protocols

To prove the efficiency of EpiDOL, we compared it with state of the art data dissemination protocols designed for VANET, namely Edge-Aware [START_REF] Nekovee | Epidemic algorithms for reliable and efficient information dissemination in vehicular[END_REF] and DV-CAST [START_REF] Tonguz | Dv-cast: A distributed vehicular broadcast protocol for vehicular ad hoc networks[END_REF]. Edge-Aware is also an epidemic protocol that utilizes the GPS information. Basically it calculates the probability of rebroadcasting P as

P = 1, if N f or N b = 0 1 -exp -α |N f -N b | N f +N b , otherwise (3)
where N f and N b are the number of times the car has received that particular message from front and from back respectively. With this approach, they have managed to give higher probabilities to vehicles near the head or tail of a cluster. However, they have not proposed a specialized function to use opposite lane that can increase the connectivity and the throughput considerably for disconnected networks as we proved in this work. Also, the proposed protocol is only compared with simple flooding protocol in a controlled scenario rather than realistic traces. DV-CAST [START_REF] Tonguz | Dv-cast: A distributed vehicular broadcast protocol for vehicular ad hoc networks[END_REF] is a distributed broadcast protocol that utilizes opposite lane vehicles. By exchanging GPS information, every vehicle classifies its network as a well connected, sparsely connected or totally disconnected network. Then depending on this classification, vehicles set the values for three flags, MDC (message direction connectivity), DFlg (direction flag) and ODC (opposite direction connectivity). For different combinations of these flags, DV-CAST takes different actions such as broadcast suppression, rebroadcast, packet relaying, carry and forward and wait and forward. However, the simulations of this protocol is only limited with controlled circular highway scenario that lacks realistic traces such as SUMO traces.

Compared to these two protocols, in EpiDOL we use not only epidemic approach but also add some intelligence by making system robust to the density changes. To achieve an objective comparison, we use the metrics defined in Section 5.

Results

We have compared the selected protocols in ns-3 with realistic SUMO traces by using 10 random runs. Our results are shown in Figures. 6789.

Fig. 6 shows the ratio of successfully received packets by all same directional nodes in terms of percentage. Error bars in the figure show standard deviations around averages. In low densities, only EpiDOL achieves more than 90% throughput. Higher opposite lane usage shown in Fig. 9 ensures higher throughput. This proves that our protocol handles the disconnected network problem more effectively than the other protocols. According to Fig. 8, end-to-end delays of all protocols are comparable. Since DV-CAST can not distribute packets to the whole network, it is expected to see lower end-to-end delays. Like Overhead of EpiDOL is lower than Edge-aware but higher than DV-CAST as shown in Fig. 7. However, in these densities our main concern is maintaining connection between disconnected networks rather than overhead.

In high densities, as seen in Fig. 6 throughput is slightly better than the other protocols. Fig. 7 shows that this improvement is realized without introducing additional overhead to the network. EpiDOL has managed to deliver packets to higher number of nodes with comparable end-to-end delays as seen in Fig. 8. Achieving higher throughputs with low overheads in high densities shows that EpiDOL was able to deal with broadcast storm problem.

Conclusion

We proposed EpiDOL which is an epidemic and density adaptive protocol for data dissemination in VANETs that utilizes the opposite lane capacity with novel probability functions. We have optimized parameters of the algorithm based on analysis by using traffic traces from SUMO. We compared the performance of the proposed algorithm with the existing algorithms in terms of end-to-end delay, overhead, throughput and opposite lane usage.

According to results in low densities we achieved more than the 90% throughput with comparable end-to-end delay, overhead and opposite lane usage. This showed that EpiDOL handled the disconnected network problem. In high densities, without excessive values in end-to-end delay and overhead, throughput achieved by EpiDOL is better than the others. This also indicates that broadcast storm problem did not effect our protocol due to its probabilistic density adaptive functions.

As a future work, we aim to focus on the analysis of the performance of EpiDOL for different data dissemination scenarios.

Algorithm 2

 2 Backward Function algorithm executed after each data packet arrives if N umberOf BackwardN eighbors ≤ backwardV alue then send with p = 1 else p = 0 end if

  End-to-end Delay Fig. 2: Choosing optimal αopposite

0 0 .Fig. 3 :Fig. 5 :

 035 Fig. 3: Throughput for Backward Functions

Fig. 8 :Fig. 9 :

 89 Fig. 6: Comparison of throughputs

Acknowledgement

This work has been conducted as part of Turk Telekom Research project under Grant Number 11315-07.