Ye Tao

Manabu Tsukada
email: tsukada@hongo.wide.ad.jp

Xin Li
email: leexin@bupt.edu.cn

Masatoshi Kakiuchi

Hiroshi Esaki
email: hiroshi@wide.ad.jp

Reproducing and Extending Real Testbed Evaluation of GeoNetworking Implementation in Simulated Networks

Vehicular Ad-hoc Network (VANET) is a type of Mobile Ad-hoc Network (MANET) which is specialized for vehicle communication. GeoNetworking is a new standardized network layer protocol for VANET which employs geolocation based routing. However, conducting large scale experiments in GeoNetworking softwares is extremely difficult, since it requires many extra factors such as vehicles, stuff, place, terrain, etc. In this paper, we propose a method to reproduce realistic results in simulation with the same software implementation. The key idea of the method is to calibrate simulator with the results of real world testbed experiments. After the simulator was calibrated, some extended experiments were carried out. Through these experiments, the fundamental functions of the GeoNetworking implementation (BTP, Greedy Forwarding, etc.) are verified, while an issue in algorithm was discovered and analyzed.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. tion of the road traffic by realizing safe, efficient and comfortable transportation. Within a number of research fields in ITS, Cooperative ITS and vehicular communications became essential for the cooperation of multiple entities in the road traffic (i.e. vehicles, roadside infrastructure, traffic control centers) in order to achieve shared objectives (safety, efficiency, and comfort).

In order to connect among vehicles and roadside units, GeoNetworking [START_REF]Vehicular Communications; GeoNetworking; Part 4: Geographical addressing and forwarding for point-to-point and point-to-multipoint communications; Sub-part 1: Media-Independent Functionality[END_REF] is employed as one of the network protocols in the ITS Station architecture [START_REF]2010 Intelligent transport systems -Communications access for land mobiles (CALM) -Architecture[END_REF], as shown in Figure 1, because the geolocation based routing features the strength in the network with dynamic topology compared with topology based routing.

GN6ASL in the ITS station architecture

With respect to the ITS station reference architecture [START_REF]Vehicular Communications; GeoNetworking; Part 4: Geographical addressing and forwarding for point-to-point and point-to-multipoint communications; Sub-part 1: Media-Independent Functionality[END_REF], the present document only affects the layer block "Networking and Transport". As depicted in figure 1, within the layer block "Networking and Transport", the present document introduces GN6ASL, an adaptation sub-layer for the transmission of IPv6 packets over GeoNetworking. The other protocols depicted in figure 1 (e.g. TCP and UDP) are represented for sake of completeness in order to represent a typical usage of the present document. In the literature, the evaluation of GeoNetworking can be performed in flexible and large scale simulated network with low cost. However mere simulations cannot provide realistic evaluation results for a specific implementation of GeoNetworking. In contrast, the experimental evaluation using the implementation in a field operational testbed gives real results in the deployment phase of GeoNetworking. Though in practice, it requires heavy cost to conduct the experiments in terms of time, manpower, space and expense. In order to take the benefits of real field test and simulation, we reproduce the results of the field experimental evaluation in the simulated network with the same implementation.

The rest of the paper is organized as follows. Section 2 highlights the related works. In Section 3, we describe our objectives in the paper. In Section 4, the experimental evaluation in the real testbed is shown. Section 5 shows the work for the reproduction of the experimentation result in the simulated networks. Section 6 extends the reproduction to the various scenarios in the simulation, and analyses an issue in the algorithm. Finally, Section 7 concludes the paper by summarizing the main results and addressing future works.

RELATED WORKS

GeoNetworking and the Implementation

Vehicular ad-hoc network (VANET) is a particular case of Mobile Ad-hoc Network (MANET), which is not restricted by the battery consumption of the communication nodes and are also characterized by the high speed movement of nodes, the availability of GPS information, and a regular distribution and predictable movements. Greedy Perimeter Stateless Routing (GPSR) [START_REF] Karp | Gpsr: Greedy perimeter stateless routing for wireless networks[END_REF] employs GPS information to forward the packets in order to adapt VANET characteristic where routes become quickly unavailable in high mobility scenarios. In GPSR, the nodes do not need to maintain part of the network structure in order to forward packets towards the destination node, but make a forwarding decision based on the destination position and neighbor positions. Within the ITS standardization domain, GeoNetworking is being completed by ETSI at the moment, integrating several geo-aware strategies to route packets better in vehicular networks.

GeoNetworking [START_REF]Vehicular Communications; GeoNetworking; Part 4: Geographical addressing and forwarding for point-to-point and point-to-multipoint communications; Sub-part 1: Media-Independent Functionality[END_REF] is standardized by ETSI as a network layer protocol as in Figure 1, integrating several geo-aware strategies including Greedy Forwarding (GF) [START_REF] Karp | Gpsr: Greedy perimeter stateless routing for wireless networks[END_REF] (Also known as GPSR), which chooses an directly reachable node which is closest to the destination based on GPS location obtained by Location Service(LS) request action, to route packets better in vehicular networks. Above the GeoNetworking, there are two different layers. One is Basic Transport Protocol (BTP) [START_REF]Intelligent Transport Systems (ITS); Vehicular Communications; GeoNetworking; Part 5: Transport Protocols; Sub-part 1: Basic Transport Protocol[END_REF] which provides basic functions of the transport layer to GeoNetworking, the other is GeoNetworking to IPv6 Adaptation Sub-Layer (GN6ASL) [START_REF]Intelligent Transport Systems (ITS); Vehicular Communications; GeoNetworking; Part 6: Internet Integration; Sub-part 1: Transmission of IPv6 Packets over GeoNetworking Protocols[END_REF] in order to enable standard IPv6 over GeoNetworking.

All the GeoNetworking nodes send beacons in a specific interval, and the neighbor nodes maintain its latest geographical location in the location table (LocT) from the received beacons. Other GeoNetworking packets delivered in the network contain the location of source (SO), sender (SE) and destination (DE); in the case that the location information in the packet is newer than the one in the location table, the location table is updated. Each location table entry (LocTE) has a lifetime counter, and the entry is removed when it is reduced to 0. When the source node does not have location of the designation in its location table, the node triggers the Location Service (LS) request message in order to obtain the location of the destination. ETSI defines the flooding based request-reply location service to get the destination location.

More than fifteen software implementations of GeoNetworking join ETSI plugtest that provides interoperability test opportunity every year. The CarGeo6 project1 provides GeoNetworking implementation in open source [START_REF] Toukabri | Experimental evaluation of an open source implementation of IPv6 GeoNetworking in VANETs[END_REF]. The GeoNetworking function and the BTP function are implemented as daemons called itsnet and btpecho, respectively in the CarGeo6 implementation as in the Figure 2. In source node, btpecho (client mode) sends a BTP packet via inter-process communication to itsnet. If the destination location is in LocT, itsnet forwards the packet to next hop selected by GF, otherwise it triggers an LS request. Finally, when the BTP echo request is forwarded to the destination, itsnet send the packet to btpecho. On the other hand, btpecho (reflector mode) in the destination node sends a BTP echo reply back to the source once it received a request. The echo reply is forwarded by GF too, thus the reply packet may be delivered via a different route from the request packet.

Experimentation and Simulation

The evaluation of GeoNetworking is performed a number of times in the simulations [START_REF] Taysi | Etsi compliant geonetworking protocol layer implementation for ivc simulations[END_REF][START_REF] Sandonis | Vehicle to internet communications using the etsi its geonetworking protocol[END_REF], because it is costly to make the experimentation in a field testbed with real vehicle integrations. There are a few experimental evaluations with real vehicles, however the number of vehicles is limited. For example, [START_REF] Anaya | Geonetworking based v2v mesh communications over wsn[END_REF][START_REF] Tsukada | AnaVANET: an experiment and visualization tool for vehicular networks[END_REF][START_REF] Tsukada | On the experimental evaluation of vehicular networks: Issues, requirements and methodology applied to a real use case[END_REF] described a field experimental evaluation performed with up to four vehicles.

Network Simulator 3 (ns-3) is an open source programmable network simulator with many capabilities. Direct Code Execution (NS3-DCE, or DCE) is a module for ns-3 to provide the ability to run Linux programs directly in its simulated environment. It enables users to do experiments with their programs in the simulated network environment without doing major source code modifications. DCE supports several types of network software infrastructures, specifically, network protocol stacks Linux. One of them is the DCE-linux protocol stack, which adapts the protocol stack of real-world Linux kernel into DCE. It has much more Layer 3 and 4 facilities than the default ns-3 protocol stack, and allows program running on it to have direct access to the MAC Layer, which is required by CarGeo6. To explain in a technical way, DCE runs several Library Linux Operating Systems [START_REF] Camara | DCe: Test the real code of your protocols and applications over simulated networks[END_REF][START_REF] Tazaki | Direct code execution: Revisiting library os architecture for reproducible network experiments[END_REF] in a single process, and connect its networking and timing backend to ns-3 facilities. The user programs can be executed in the simulated library OS efficiently.

OBJECTIVE AND APPROACH

Experimentation in real testbed using GeoNetworking implementation can provide very precise evaluation result in the deployment phase of the it requires heavy cost (i.e. vehicles, equipment, drivers, time, etc.). On the other hand, the simulation can provide the evaluation result in flexible networks with various scenarios in a low cost, however the result is based on the preconfigured model in the simulation and often diverse from the experimental evaluation in the real testbed.

Our objective is to investigate realistic behavior of GeoNetworking by simulation in various scenarios. To realize the objective, we take the benefits of both experimentation in real testbed and simulation in the following approach as in Figure 3. In this paper, we combine experimentation in real testbed and simulation. Firstly, we conduct experimentations in real testbed using an open source GeoNetworking implementation (Section 4). Secondly, we reproduce the experimental evaluation result of GeoNetworking in the simulated network using the same implementation (Section 5). Finally, we extend the simulation to a large scale network with various scenarios (Section 6).

The method developed in the paper has three aims. First, the developers of the GeoNetworking implementation can understand the realistic behavior of the software in large scale networks under various scenarios. Second, by understanding the behavior of the implementation, it eases the debugging and the performance improvement of the implementation. Last, it facilitates the development of ITS applications working on the GeoNetworking implementation.

EXPERIMENTAL EVALUATION IN REAL TESTBED

We performed an experimental evaluation using CarGeo6 version 0.9.9, on 4 ARM boxes with real hardware specifications as listed in Table 1. A mac filter was applied on each node, to limit the neighbour that it could communicate with adjacent nodes only, achieving an up to 3 hop topology as depicted in Figure 4(a). The round-trip times (RTTs) are measured between the source and the destination in the case from single hop to 3 hops with various packet sizes (varying from 20 bytes to 1500 bytes by increasing the size by 20 bytes). The btpecho (client mode) sent the BTP echo request 100 times to the btpecho (reflector mode) in each test. There is no traffic besides the echo request, echo reply and the beacons during the tests.

Figure 6(a) shows the result of average RTT in the experimental evaluation (In order to save the space of the paper, the figure also shows the uncalibrated reproduction results which is explained in Section 5).

The RTT increases along with the packet size in all tests (from single hop to 3 hops). When the packet size is 20 bytes, RTT on 2 hop and RTT on 3 hop have 1.5 ms and 3.4 ms greater than the one in single hop, respectively; when packet size is 1380 bytes, they are 5.3 ms and 10.5 ms greater. BTP GeoNetworking does not process the packet bigger than the MTU because the fragmentation is not defined in the specification. Therefore all the packets bigger than 1380 bytes were lost in the experiments.

REPRODUCTION OF REAL TESTBED RESULT IN SIMULATED NETWORKS

In the last section, we described how the experiments are done in the real testbed. Nevertheless, the real testbed has limitations: high cost, limited scale, inflexible in configuration, etc. In order to overcome these limitations, a realistic simulated environment called Direct Code Execution was employed. With minor and trivial modifications to the Car-Geo6 source code as well as some parameter calibrations, we successfully reproduced the real testbed results in the simulated environment. In this section, we describe the successful reproduction of the real testbed results in the simulated environment.

Simulation configuration

Ns-3 and DCE has many parameters which can be tuned to reproduce the real testbed environment. In order to tune and calibrate the simulator, we use a simple linear topology which is shown in the Figure 4(a): all nodes are configured with the same Wi-Fi parameters, and kept in a same Ad-Hoc cell; each node are in a line with 300m distance to adjacent node. With a negative receiver antenna gain, the wireless radio range is adjusted to 300-400 meters. That means nearly all packets in 300m range can be delivered, yet nearly all packets from 400m away were lost. The configuration ensured each node can and can only reach the adjacent nodes. The detailed configuration in DCE is shown in Table 2. Since the CarGeo6 is not fully compatible with libc and kernel used in DCE, some modifications in the code must be done. To Avoid messing up the original code and keeping backward compatibility, a separate module called dce compat is employed. Most of the compatibility code goes to the compatibility module, and modifications in other modules are minimized and the backward compatibility are maintained. The modifications can be divided into 4 parts:

Socket Incompatibilities

DCE provides a limited subset of socket features. However, several ones which used in CarGeo6 are not supported. Sequenced packet socket, which is one of it, must be modified to Streaming socket, with manually framing. Another one is peeking read in socket, which should be cared manually.

Timing Function Incompatibilities

Another big issue is about timing. DCE does not support monotonic clock, which is considered rigorous in timing. Moreover, minimum resolution of is microsecond instead of nanosecond in modern operating systems. Some workarounds are employed in dce compat. E.g., using real time clock instead of monotonic ones. Consequential bugs in CarGeo6 are fixed.

Lack of Math Functions

Several math functions lacked in DCE are mandatory for geolocation calculation, which includes sine, cosine, arctangent, square root, absolute value, etc. These functions are implemented by hand in dce compat module, and will be contributed to upstream of DCE.

Lack of Other Functions

Besides math functions, DCE lacks some other functions such as BSD compatible functions, some of pthread features, etc. Some of them can be skipped safely, while the others are implemented manually in dce compat.

Packet Size issue in DCE

During preliminary experiments, we noticed an issue regarding the RTT, which leads to the steps as depicted in Figure 5. We inspected this issue and found it is caused by a hack of link layer packet size, which is located in the Linux kernel of library OS used by DCE2 . The hack is an attempt to solve an issue in TCP, therefore it is not needed for GeoNetworking reproduction. We had modified the code to bypass the hack, and got perfect results as shown in

Processing Delay in DCE

In networking, processing delay means the time for a device to process a packet, which can affect the result of experiment. In Linux kernel, the cause of processing delay is rather complicated and can be affected by many factors, including task scheduling, interrupt handling, Wi-Fi antenna delay, etc. That means, fully modeling processing delay in DCE is impossible.

The result of CarGeo6 reproduction was greatly impacted by it. An approach must be carried out to calibrate it. DCE has some facilities to model the processing delay of each simulated operating system through the task scheduling. We simplified the model by aggregating other factors into task schedule delay.

Detection and Analysis

The processing delay issue was first detected in preliminary experiments, when we were trying to reproduce the real testbed results in simulated environment. With the same hardware parameters, our experiments reproduced fairly realistic results, shown in Figure 6(a). However, a constant difference was observed between real and reproduction results.

In the figure, we noted that the delay is approximately in proportion to the number of nodes invoked. By this evidence, the possibility of propagation delay can be ruled out since it is related to number of hops. To classify, there are two types of nodes in these experiments which should be considered separately, as in Figure 2:

Intermediate node only executes itsnet program and in charge of packet routing and forwarding. Define Dn as the total observed delay in n-hop case (n + 1 nodes in total), DT , DI as the proportion of observed value from terminal node and intermediate node respectively, while PT , PI for the parameters of internel task scheduling delay in DCE.

In the configuration of in Figure 4(a), the relationship among Dn, DT and DI should follow equation 1:

Dn = 2 × DT + (n -1) × DI (1)

Calibration

Several steps are taken carefully to calibrate the processing delay in the nodes:

Pre-calibration Calculation Before calibration, we did a refined measurement and calculation on the observed difference in each experiment, which is shown in With a preliminary test, we found there were no clear relationship between PT and DT , nor PI and DI .

Step I: Calibration of terminal node Calibrate the PT on single-hop configuration. We did a binary search for the PT , and evaluate the difference between simulation and testbed results. Optimal value were found at 200 microseconds: in Figure 6(b), the simulation line overlaps with the testbed one.

Step The simulation line is close to the testbed one, which verifies our conjecture. The fact that PT << PI while DT > DI is reasonable: the terminal nodes have heavier load than intermediate nodes, the Kernel scheduling was done more time, thus the delay should be lower.

After proper calibration, the results practically overlap the real ones, with maximum absolute error of 60 microseconds.

The results suggest that our reproduction with calibration is credible, thus it can be extended to large scale network scenarios.

EVALUATION IN LARGE SCALE AND FLEXIBLE SIMULATED NETWORKS

In this Section, we provide preliminary evaluation results regarding the performance of the implementation from the point of RTTs, with varied node distances,packet sizes and number of hops. We define Node distance (ND) as the distance between adjacent nodes, Terminal Distance (TD) as distance between destination and source nodes.

With the calibration made in Section 5, we conducted several experiments. First, experiments with customized number of hops were conducted to evaluate the Greedy Forwarding algorithm, which shows that the algorithm successfully to choose a multi-hop route to forward packets up to 9 hops. Then, ND and TD are modified to examine their impact on the network, and we found there could be a extremely high packet loss under certain conditions.

Extended scenarios

We successfully extended the experiments in NS3-DCE with the topology as depicted in Figure 4(a), with any desired number of nodes (N ), and varied NDs which is difficult under real testbed with limited manpower and resources. The topology with ND of 100m is shown in Figure 4(b), when it comes to 10m or 50m, the topologies will be slightly different. Thus we can examine how packet size and number of hops affect the results in reproductions, and whether the implementation can be properly functional with varied NDs and TDs. Therefore, the prediction of realistic behavior of the implementation is viable.

We first measured the network delay perceived by the btpecho initiator with packet size range from 20 bytes to 1380 bytes in a single hop. Then, extend to 2, 3 and finally 9 hops, and repeat the first experiment. Finally, compare and analyse the data obtained in reproduction to find out how packet size and number of hops impact the network delay. For all delay measurements we measured 1000 BTP echo Request RTTs between the two terminal nodes with interval time of 0.5 seconds.

More hops

RTTs of different hops and packet sizes were obtained from reproduction, as depicted in Figure 7. The RTT increases as the packet size increase as we have in the previous sections. With the packet size of 20 bytes, the RTT increases by 1.55ms each hop increase. When the packet size comes to 1380 bytes, the increment of RTT is 5.23ms each hop.

Different node distances

We select some data with some specific NDs and fixed packet size of 80 bytes, but with varied TDs, as depicted in Figure 8. Figure 4(b) depicts a specific scenario with N D = 100m. The impact of ND and TD on realistic behavior of the implementation can be predicted through the data.

With the 10m ND, it can be observed that RTTs are constant regardless of TD. It indicates the GF algorithm selected the terminal node directly, thus the number of hops is 1. With the 100m ND, when TD rises, a notable rise of RTT can be observed when TD rises from 300m to 400m, which indicates that the GF algorithm worked in the reproduction to forward the packets via a multi-hop route when the destination is out of its radio range. With the 300m ND, a tendency can be observed that, the network delay presents a perfect linear rise, with the growth of TD, which can be considered as the number of hops with such large enough ND. The result shows the tendency that how hop of the route changes with different NDs and TDs.

M inimalRequiredHops = T D/RadioRange

(2)

Packet loss issue in Greedy Forwarding algorithm

During experiments, a packet loss was detected when Node 0 try to send BTP echo request to Node 4, same is the Node 0 try to response, with a particular scenario that 4 nodes in a line, ND is 100m and TD is 400m. In this Section, we discuss the cause of the packet loss, and how to quantify it.

High PLR(Packet Loss Ratio) in long distance

As described in Section 5, Friis Propagation Loss Model is employed in the reproduction, making the communication channel unstable; this is the scenario we've not realized in the real testbed yet. In this reproduction, the maximum stable transmitting radius of a node is in the range from 300m to 400m. Thus Node 4 is at the edge of the transmitting radius of Node 0, which means the communication channel between them is unstable. With an extremely unstable channel, beacon messages from Node 4 to Node 0 are unexpected. An unexpected beacon makes a location table entry that can hardly be reached as in Figure 9.

Analysis

As depicted in Figure 9. Consider the following scenario: Alice (Node 0) wants to communicate with Bob (Node 4), the channel between them is unstable due to the distance or obstacle. But Carol (an intermediate node) has good communication channels with Alice and Bob without message loss. The three repeatedly broadcast beacon message to inform each other their existence. However Alice still can receive some beacon messages from Bob randomly with a loss ratio of P LRB. When Alice gets any message (usually a beacon message) directly sent from Bob, she will instantly label Bob as her Neighbour in her LocT as an entry with a default lifetime (T(LocTE)) of 20s. And then, Alice will directly send messages to Bob; the packet could be lost, with a possibility of P LRP . Otherwise, in order to reach Bob, Alice will deliver her message to Carol, letting her to forward the message to Bob, with no loss.

In a particular reproduction, Alice received 41 out of 3254 unexpected beacon messages from Bob, which is founded in the log file. 4 among the 41 total beacon messages from Bob is encountered during the BTP echo operation. Remember, P LRP is the loss ratio of BTP echo packet, which is supposed to be higher than P LRB due to different packet length. To simplify, assume that P LRP = 0, and the unexpected beacon message effective periods(20s) on both sides do not collide or overlap with each other. With the BTP echo interval of 0.5s, there is supposed to be 40 packet losses every unexpected beacon message encountered. Thus, there is supposed to be 4 × (20/0.5) = 160 packet losses in the experiment, and it is in accordance with the result. With N as the total number of beacons from a sender (SE), N d as the number of delivered beacons from a sender (SE). Then, do an preliminary calculation on the beacon packet lost ratio in equation 3:

P LRB = 1 -(N d /N ) (3)
With N d = 41, N = 3254, we get a loss ratio of 98.7%. In a statistics point of view, assume fB as the frequency of beacon message, T as the lifetime of LocTE, then we have the expectation of loss ratio in total: total loss = P LRP × (1 -P LR

(T ×f B ) B ) (4)
To simplify again, assume P LRP = 100% when P LRB is close to 100%. Hence the loss ratio is only connected with fB, P LRB and T . According to the GeoNetworking implementation, each node broadcasts a beacon message every a little bit longer than 3 seconds. With fB = 1/3 Hz, P LRB = 0.987, T = 20 s, then we have the loss ratio of 8.35%. Recall the simplification we made, the observed value may be slightly different. The observed value of 7.3% in the reproduction proved this point. According to the Equation 4, the packet loss could be extremely high under certain condition as depicted in Figure 9. Another reproduction proved it, and it will be discussed in further research.

CONCLUSIONS AND FUTURE WORKS

In this paper, we have successfully reproduced real testbed experiments in simulated network environment using NS3-DCE. With the proper wireless configuration and the calibrations, the reproduction produced realistic results, which can be used to predict the behavior of GeoNetworking implementation in real world. Based on the fact, several extended experiments of the GeoNetworking was conducted with NS3-DCE. The results indicate that, the implementation succeeded in delivering packets up to 9 hops with any desired number of nodes; meanwhile the GF algorithm functions properly; yet a packet loss is observed in configurations of a critical communication distance. Finally we quantified the packet loss, and found it is only related to 4 factors: beacon frequency, lifetime of LocTE and PLR of beacon and BTP; moreover, it could be extremely high under some certain conditions.

As a future work, we consider the followings: First, more factors should be introduced to the reproduction E.g., a moving mobility model, a complicated 2D distribution of nodes, other communications modes. Second, IPv6 over GeoNetwork should be ported to NS3-DCE for more extended experiments. Last, more efficient routing strategy can be evaluated in the simulated networks, to solve the packet loss issue discovered in the extended scenarios. it is worth discussing whether the current routing strategy is the best and most efficient.

  (ITS) aim at optimiza- * Graduate School of Information Science and Technology, The University of Tokyo † School of Information and Communication Engineering, Beijing University of Posts and Telecommunications ‡ Information Initiative Center, Nara Institute of Science and Technology

  CFI '15, June 08 -10, 2015, Seoul, Republic of Koreac 2016 Copyright held by the owner/author(s). Publication rights licensed to ACM. ISBN 978-1-4503-3564-5/15/06. . . $15.00 DOI: http://dx.doi.org/10.1145/2775088.2775092

Figure 1 :Figure 1 :

 11 Figure 1: GN6ASL in the ITS station architecture As depicted in figure 1, the present document builds an adaptation sub-layer (GN6ASL) between the ETSI GeoNetworking protocol [i.25] and an IPv6-compliant protocol layer and extended with mobility extensions. The default IPv6 mobility extensions in the ETSI ITS architecture [1] (as well as in [i.19]) is the Network Mobility Basic Support (NEMO BS) protocol [15]. The present document enables the usage of NEMO BS over the ETSI GeoNetworking protocol [i.25]. NOTE: With respect to the figure 1, the scope of [i.19] includes the protocol layer IPv6 + Mobility Extensions, directly above the adaptation sub-layer specified in the present document.

Figure 2 :

 2 Figure 2: Overview of CarGeo6 programs

Figure 3 :

 3 Figure 3: Our approach

5. 2

 2 Figure 4: Topologies

Figure 5 :

 5 Figure 5: Wrong RTTs cause by Packet Size issue

Figure 6 :

 6 Figure 6: Processing Delay Calibration Steps

Fig- ure 6

 6 (a). The result was slightly different from proportional expectation: D1 = 1700 us, D2 = 2450 us, D3 = 3200 us. Thus DT = 850 us, DI = 750 us.

  II: Calibration of intermediate node After Step I, the shift distance of the simulation result of two-hop configuration is the same as one hop, as shown in Figure 6(c). Calibrate the PI on two-hop configuration, with PT = 200 us. Another binary search was carried out to find the optimal value: 540 microseconds. Post-Calibration Verification Verify the three-hop result with PT = 200 us and PI = 540 us, as shown in Figure 6(d).

Figure 8 :

 8 Figure 7: Average RTT in Simulation

  pa ck et lo ss up to 70 % about 30% deliver ratio

Figure 9 :

 9 Figure 9: Unexpected Beacon loss

ETSI ETSI TS 102 636-6-1 V1.1.1 (2011-03) 13

	PDCP	Packet Data Convergence Protocol
	PHY	PHYsical
	PPP	Point-to-Point Protocol
	RA	Router Advertissement
	SAP	Service Access Point
	SEND	SEcure Neighbor Discovery
	SLAAC	StateLess Address AutoConfiguration
	SMI	Structure of Management Information
	SNAP	SubNetwork Access Protocol
	STA	STAtion
	TAP	Terminal Access Point
	TCP	Transmission Control Protocol
	TUN	network TUNnel
	TVL	Topological Virtual Link
	UDP	User Datagram Protocol
	UMTS	Universal Mobile Telecommunications Systems
	VC	Virtual Circuit

Table 1 :

 1 System configuration

	Item	Specification
	CPU	Dual Core ARM11 600MHz SoC
	Memory	128 MB RAM
	Storage	16 MB Flash
	Kernel	Linux kernel 2.6.35.13
	MAC protocol	IEEE 802.11p (ETSI G5)
	Wireless Interface Unex DCMA-86P2

Table 2 :

 2 DCE network configuration

	Item	Specification
	Radio Frequency	5.9GHz
	Wi-Fi Phy	ERP-OFDM, 6Mbps
	Wi-Fi Mac	Ad-Hoc
	Receiver Gain	-10dBi
	Propagation Delay	Constant
	Propagation Loss	Friis
	Node Mobility Model Static

http://www.cargeo6.org/

https://github.com/direct-code-execution/net-next-sim/ blob/sim-ns3-3.14.0-branch/arch/sim/sim-device.c#L130


Hafid Ait-Oufella

Alain Tedgui
email: alain.tedgui@inserm.fr

Regulatory T cell plasticity: another layer of complexity in atherosclerosis

During the last 2 decades, human and animals studies have clearly documented the crucial role of inflammation in the development and complications of atherosclerosis [START_REF] Ait-Oufella | Adaptive (T and B cells) immunity and control by dendritic cells in atherosclerosis[END_REF] . Both innate and adaptive immunity are involved in this process. The first evidence that suggested a role of adaptive immunity in atherosclerosis was the widespread detection of the major histocompatibility class II in human atherosclerotic plaques, and the presence of a large amounts of CD3 + lymphocytes in human and mouse atherosclerotic lesions. Most of T cells in mouse and human atherosclerotic plaques are CD4 + T-helper(Th) cells expressing the αβ Tcell antigen receptor (TCR).Among CD4 + T cells, Th1 cells have been shown to exert proatherogenic effects, whereas regulatory T cells (Tregs) display atheroprotective properties.

The role of Th2 and Th17 cells is still debated [START_REF] Ait-Oufella | Adaptive (T and B cells) immunity and control by dendritic cells in atherosclerosis[END_REF] . Helper T cell subsets are defined by the production of cytokines and/or the expression of characteristic lineage-defining transcription factors. Th1 cells are generated upon priming in the presence of IL-12 that promotes the expression of the transcriptionfactor T-bet and stimulatesthe production of the Th1 prototypical cytokine IFN-γ 2 . Tregshave a central role in the dominant control of immunologicaltolerance and maintenance of immune homeostasis. They were first identified in miceand later in humans [START_REF] Sakaguchi | Naturally arising Foxp3-expressing CD25+CD4+ regulatory T cells in immunological tolerance to self and non-self[END_REF] . The transcription factor FoxP3 is essential for the generation and the functions ofTregs [START_REF] Fontenot | Regulatory T cell lineage specification by the forkhead transcription factor foxp3[END_REF] . FoxP3 deficiency leads to a multi-organ autoimmune disease as can be observed in the "scurfy" mouse and in human immune dysregulation, polyendocrinopathy, enteropathy X-linked syndrome (IPEX) patients [START_REF] Bennett | The immune dysregulation, polyendocrinopathy, enteropathy, X-linked syndrome (IPEX) is caused by mutations of FOXP3[END_REF] . Natural or thymic FoxP3 + Tregs acquire regulatory lineage commitment already upon maturation in the thymus [START_REF] Sakaguchi | Naturally arising Foxp3-expressing CD25+CD4+ regulatory T cells in immunological tolerance to self and non-self[END_REF] , whereas adaptive or peripheral Foxp3 + Tregs can be induced from mature CD4 + Thcells in the periphery under the influence of different stimulations, especially TGF- [START_REF] Fantini | Transforming growth factor beta induced FoxP3+ regulatory T cells suppress Th1 mediated experimental colitis[END_REF] . Other subsets of Tregs that do not express FoxP3 have been described. Tr-1 cells are characterized by the production of large amounts of IL-10, a potent anti-inflammatory and anti-atherosclerotic cytokine, and IL-10dependent suppression of T cell responses [START_REF] Groux | A CD4+ T-cell subset inhibits antigen-specific T-cell responses and prevents colitis[END_REF] . In atherosclerosis, Tregs and Tr-1 cells exert protective effects. Significant acceleration of atherosclerosis has been observed in mice with reduced Treg cell numbers, as obtained by invalidation of CD80/86, CD28, ICOS or after treatment with CD25-depleting antibodies 8 ,9 . Other approaches for Treg cell depletion, including antisense-induced Treg cell apoptosis, vaccination of mice against FoxP3, or use of mice expressing the diphtheria toxin receptor under control of the FoxP3 promoter 10 , all concluded to increased vascular inflammation and atherosclerosis in the absence of Tregs. In contrast, adoptive transfer of CD4 + CD25 high Treg cells [START_REF] Ait-Oufella | Natural regulatory T cells control the development of atherosclerosis in mice[END_REF] or IL-10 producing Tr-1 cells 11 reduced atherosclerotic lesion development in Apoe -/-mice.

Th cells express a « lineage-specific » transcription factor that controls their generation, effector cytokine production, phenotype and function, and prevents the differentiation to an alternative lineage. Nevertheless, it has become increasingly clear that cells belonging toa specific Th lineage are notexclusively terminally differentiated cells, but that some maintain a certain degree of plasticity. Mature CD4 + T cells can acquire characteristicsof alternative lineages upon antigen restimulation [START_REF] Geginat | Plasticity of human CD4 T cell subsets[END_REF] . For example, IL-12can induce IL-10 production by Th1 cells and IFN-γ/IL-10 co-producing T cells with regulatory functions have been reported in peripheral blood of healthy donors [START_REF] Haringer | Identification and characterization of IL-10/IFN-gamma-producing effector-like T cells with regulatory function in human blood[END_REF] . IFN-γ/IL-10 co-producing T cells could also be generated after stimulation of Th17 cells in the presence of IL-12 or IL-27 [START_REF] Heinemann | IL-27 and IL-12 oppose pro-inflammatory IL-23 in CD4+ T cells by inducing Blimp1[END_REF] . Recent findings suggest that FoxP3 + Tregsalso show functional and phenotypic plasticity, beingable to secrete proinflammatory cytokines. For instance, Tregs expressing the T-bet and the Th1-associated chemokine receptor CXCR3 canswitch to a Th1 program and accumulate at sites of Th1 inflammatory responses [START_REF] Koch | The transcription factor T-bet controls regulatory T cell homeostasis and function during type 1 inflammation[END_REF] .

In this issue of Circulation Research, Li et al. report for the first time data showingthat more than40%of CD4 + T cellsin atherosclerotic aorta of Apoe -/-mice under high fat dietexpress CCR5and exhibit a unique repertoire of molecular and cell surface markers, including positivity for FoxP3,T-bet, and IFN-γ, but are CD25negative.These so-called « CCR5Teff » cells use CCR5 and its ligand CCL5 to home to the aorta and interact with CD11c + antigen presenting cells there.

Interestingly, it is now well accepted that a transcription factor is not the « sole » requisite signature that determines a specific Th lineage. Foxp3 transduction by itself is not sufficient to completely recapitulate the Treg-transcriptional profile. This conclusion issupported by studies employing Tregs with non-functional Foxp3, which demonstrated that not all Foxp3 + T cells are functional Tregs, and that part of the Treg signature can be induced in the absence of FoxP3 [START_REF] Ohkura | Treg cells acquire new directions, cytokines navigate[END_REF] . In their study, Li et al. have shown that Foxp3 + CCR5Teff were able to significantly reduce IFN-γ, IL-4, IL-13, Il-17A production by effector T cells,but were unable to suppress effector T cell proliferation.This might be accounted by theinability of CCR5Teff to suppress IL-2 secretion. As the proliferation of FoxP3 + CCR5Teff cells themselves in the absence of effector T cells was not reported, it is difficult to classifythis vascular-located T cell population as true effector T cells that shouldproliferate in response to coated anti-CD3 or when co-cultured with mature antigen-presenting cells, whereas regulatory T cellsshould not. IL-2 is required to induce CD25 high Treg cell proliferation in vitro. So-called FoxP3 + CCR5 Teff cells displaysome features of previously described Th1-like regulatory population that co-expresses FoxP3, T-bet andIFN-γ. In the context of airway hyper-reactivity, it has been shown that T-bet + FoxP-3 + T cells induced by CD8 + dendritic cells canproduce both IL-10 and IFN-γ [START_REF] Stock | Induction of T helper type 1-like regulatory cells that express Foxp3 and protect against airway hyperreactivity[END_REF] . In the intestine, the conversion of FoxP3 + Tregs into FoxP3 + IFN-γ + T cells, requiring IL-12 production by antigen-presenting cells, have also been described [START_REF] Feng | Interleukin-12 converts Foxp3+ regulatory T cells to interferon-gamma-producing Foxp3+ T cells that inhibit colitis[END_REF] . However, in both studies the so-called "Th1-like Tregs" showed strong suppressive functions. They blocked effector T cell expansion and protected against organ specific inflammation. In the study by Li et al., the adoptive transfer of FoxP3 + CCR5Teff cells into Ccr5 -/-Apoe -/-micewas not protective, and waseven able to accelerate atherosclerosis to the same extentas "conventional" effector T cells.Therefore,in vitro and in vivo experiments rule out the possibility that FoxP3 + CCR5Teff cells are Th1-like Tregs, and suggest thatthese cellsdisplay an effector T cell phenotype. This was supported by comprehensive transcriptome analysis.Effector T cell population expressing FoxP3 had never been described in mouse before, but had been previously reported in activated human T cells with unstable expression of FoxP3, whichdid notacquire suppressive function [START_REF] Hennezel | Single-cell analysis of the human T regulatory population uncovers functional heterogeneity and instability within FOXP3+ cells[END_REF] or genetic blocking of the CCL5-CCR5pathway in T cellsreduced their homing into explanted aorta, butin vivo evidence for specific migratory and pro-atherogenic properties of the newly discoveredCCR5 + Teff cell populationwouldhave been more striking.They showed that the adoptive transfer of CCR5Teffcells in Ccr5 -/-Apoe -/-miceaccelerated atherosclerosis. Yet, the same effect was observed following the adoptive transfer of "conventional" effector T cells that expressed CCR5 at amuch lower level. It would have been of great interest to see whether thenumber of CCR5 + Teff cells that accumulatedin the vascular wall was higher than that of"conventional" effector T cells, which would strongly argue in favor ofa powerfulmigratory propertyof CCR5 + Teff into atherosclerotic lesions. Finally,interestingly,CCR5 + Teff cells were detected in the aorta and the draining lymph nodes, but not in the spleen, suggesting that their mature phenotype was acquired locally. Further studies are required todetermine the specific stimulatory pathway, antigen-dependent or antigen-independent,whichwas involved in CCR5 expression on CCR5 + Teff cells.

Collectively, knowledge gained from the present study by Li et al. about CCR5 as the major homing receptor for CD4 + T cells into atherosclerostic lesions will help to develop optimal therapies that either undermine pro-atherosclerotic effector T cells, or enhance the development ofstable and anti-atherogenic Tregs for immunotherapy.

Sources of funding

INSERM

Disclosures

None

Figure Legend

Almost all of the CCR5 + T cells in aortas from Apoe -/-mice coexpress IFN-γ, T-bet and low level of FoxP3. These« CCR5Teff » cells are concentrated in the aorta, with some making their way to the para-aortic lymph nodes. They produce high levels of IFN-γ, TNFα, IL-10 and IL-2, reducethe secretion of IFNγ, IL-4, IL-13, IL-6 and IL17A (but not IL-2 or IL-5), by effector CD4 + CD25 -T cells, but do not suppress the proliferation of the latter. Natural CD4 + CD25 hi FoxP3 + Tregsinhibit effector Tcell proliferation and cytokine secretion.

  .Li et al. clearly documentedthat FoxP3 expression was very low in CCR5Teff cells, 5-6 fold lower thanin CD4 + CD25 high Tregs. In human, Miyara et al. characterized Treg cellsubsets according to theirFoxP3 expression 20 . They identified 3 different populations: CD45RA + FoxP3 low resting Treg cells, CD45RA -FoxP3 high activated Treg cells, and cytokine-secreting CD45RA -FoxP3 low non-Treg cells. Interestingly, the latter population had the same secretive and functional profileas the mouse CCR5Teff identified by Li et al. They both produced IL-2, IFN-γ and displayed no suppressive functions. Li et al. alsoexplored the role ofCCR5 onCD4 + T cells trafficking.Several studies have previously reported the pro-atherogenic role of CCR5, but most focused on monocytes. Pharmacological inhibition 21 or genetic invalidation ofCCR5significantly reduced monocytosis and atherosclerosis. CCR5 blocking prevented macrophage infiltration in the lesions and to a lower extent T cell infiltration.Li et al. reported that in vitro, pharmacological


Vincent Minier

Gilles Durand

Pierre-Olivier Lagage

Michel Talvard

Tony Travouillon

Maurizio Busso

Gino Tosti

SUBMILLIMETRE/TERAHERTZ ASTRONOMY AT DOME C WITH CEA FILLED BOLOMETER ARRAY

astronomy is the prime technique to unveil the birth and early evolution of a broad range of astrophysical objects. A major obstacle to carry out submm observations from ground is the atmosphere. Preliminary site testing and atmospheric transmission models tend to demonstrate that Dome C could offer the best conditions on Earth for submm/THz astronomy. The CAMISTIC project aims to install a filled bolometer-array camera with 16x16 pixels on IRAIT at Dome C and explore the 200-µm windows for potential ground-based observations.

1 Submm astronomy: science drivers and novel bolometer technology Submillimetre (submm) astronomy is the prime technique to unveil the birth and early evolution of a broad range of astrophysical objects. It is a relatively new branch of observational astrophysics which focuses on studies of the cold Universe, i.e., objects radiating a significantif not dominantfraction of their energy at wavelengths ranging from ∼ 100 µm to ∼ 1 mm. Submm continuum observations are particularly powerful to measure the luminosities, temperatures and masses of cold dust emitting objects . Examples of such objects include star-forming clouds in our Galaxy, prestellar cores and deeply embedded protostars, protoplanetary disks around young stars, as well as nearby starburst galaxies and dust-enshrouded high-redshift galaxies in the early Universe (Fig. 1).

Observations of submm continuum emission are usually carried out with bolometer detectors. Recently, CEA (DSM/DAPNIA/SAp and DRT/LETI) has developped a novel bolometer technology for the PACS submm/far-infrared imager on 0.3 pc Fig. 1. Left: NGC7538, a high-mass star-forming region at 2.6 kpc. The grey contours are the 1.2 mm continuum emission that was imaged with MAMBO-2/IRAM-30m and overlaid on the DSS optical image of the HII region. High-mass protostellar objects are localed in the mm emission clumps to the south of the HII region (Pestalozzi et al. 2006). As high-mass protostellar objects emit the bulk of their energy between 60 and 400 µm (Minier et al. 2005), submm/THz continuum mapping is a unique tool to study them. Right: Spectral Energy Density of ultra-luminous galaxies for redshift z=0.1 to 5. Submm/THz continuum observations measure SED peaks for z=3 to 5 (Guiderdoni et al. 1998).

the Herschel Space Observatory. The R&D was based on a unique and innovating technology that combines all silicon technology (resistive thermometers, absorbing grids, multiplexing) and monolithic fabrication. The bolometers are assembled on a mosaic "CCD-like" array that provides full sampling of the focal plane with ∼ 2000 pixels that are arranged in units of 256 pixels. They are cooled down to 300 mK to optimise the sensitivity down to the physical limit imposed by the photon background noise. The PACS bolometer arrays have passed all the qualification tests [START_REF] Billot | Proceedings of the SPIE[END_REF]. The newly started ArTéMiS project at CEA Saclay capitalises on this achievement by developing submm (200-450 µm) bolometer arrays with ∼ 4100 pixels for ground-based telescopes. A prototype camera operating in the 450 µm atmospheric window has successfully been tested in March 2006 on the KOSMA telescope [START_REF] Talvard | Proceedings of the SPIE[END_REF].

Placed on a 12-m single-dish telescope that is located on a dry site (25% PWV<0.5 mm) , a bolometer camera with ∼4100 pixels at 200-450 µm will be particularly powerful to undertake wide field surveys of star-forming complexes in our Galaxy as well as deep field surveys of dust-enshrouded high-redshift galaxies in the early Universe (see [START_REF] Talvard | Proceedings of the SPIE[END_REF] for an overview of ArTeMiS science cases). Beside Herschel and ALMA, large-format submm imagers will provide (1) better angular resolution than Herschel around ∼ 200 µm and (2) wider-field (Pardo et al. 2001) for Chajnantor with PWV equal to 0.1 mm (green), 0.2 mm (yellow), 0.5 mm (red) and 1 mm (blue). The solid line represents the atmospheric transmission expected at Dome C for PWV=0.17 mm (Lawrence 2004).

mapping capabilities than ALMA, making large-scale surveys possible.

Atmosphere transmission: Chajnantor vs. Dome C

A major obstacle to carry out submm observations from ground is the atmosphere. Astronomical observations in the submm/THz spectral bands (e.g. 200, 350, 450 µm) can only be achieved from extremely cold, dry and stable sites (e.g. high altitude plateau, Antarctica) or from space (e.g. the Herschel Space Observatory) to overcome the atmosphere opacity and instability that are mainly due to water vapour absorption and fluctuations in the low atmosphere. Chile currently offers the best accessible (all-year long) sites on Earth, where the precipitable water vapour (PWV) content is often less than 1 mm. Chile hosts the best astronomical facilities such as ESO VLT, APEX and the Chajnantor plateau will be the ALMA site. At longer term, and particularly if global warming severely restricts the 200-350-450-µm windows on ESO sites, Antarctica conditions with less than 0.2 mm PWV, could offer an exciting alternative for THz/submm astronomy (Fig. 2). This is an attractive opportunity for the 200-µm windows, especially, which are normally explored with Space telescopes (e.g. Herschel).

In order to determine the respective performances of a submm bolometer array at Chajnantor and Dome C, we have modeled the atmospheric transmisssion of the two sites. of Chajnantor, the PWV statistics available in Peterson et al. (2003) were used. These statistics span a total period of ∼ 5 years. In the case of Dome C, no PWV data is available for a long period of time including winter. The closest site where a large amount of statistics are available is the South Pole. The difference between the South Pole and Dome C, in terms of PWV are expected to be small. Transmission measurements in the summer time at 350 µm made at both sites by [START_REF] Calisse | [END_REF] suggest that the transmissions are similar while Dome C has better stability than the South Pole. For this model, we therefore use the PWV data from the South Pole available in Peterson et al. (2003) and extrapolate the corresponding atmospheric transmission at Dome C using the model in Lawrence et al. (2004). Our transmission calculations for Dome C must therefore be considered conservative.

Fig. 3-left shows the results of the transmission statistics at Dome C and Chajnantor for 450 µm. At this wavelength, the transmission of the two sites is only comparable for the best 10 percent of the time. For non-exclusive use of telescope time for this wavelength, Chajnantor is therefore the optimal site as submm observatories (e.g. APEX, ASTE, NANTEN-2) are already in place and is therefore cheaper than Dome C were no sub-mm facilities are currently available. For exclusive use of telescope time for the 450 µm window, Dome C becomes however a more attractive site. This window remains above a transmission of 50% for more than 90% of the time while the transmission at Chajnantor drops far quicker to a median value of 35%. At 200 µm, this difference is even more severe. As shown in Fig. 3-right, observing from Chajnantor requires exceptional conditions on the APEX site at this wavelength. Even at Dome C, the best conditions are required to attempt the observation of bright sources.

The stability of these windows is an equally important parameter when comparing the sites. Fast variations of the PWV mean that the transmission can vary significantly during an observation. Peterson et al. (2003) have calculated the statistics of the standard deviation of the PWV over periods of 2 hours (typical observation time) for South Pole and Chajnantor. Using the 25% and 50% percentile of the distribution of this number for the two sites, we have calculated the corresponding standard deviation of the transmission at 200 and 450 µm as a definition for stability and applied the South Pole values to Dome C modeled transmission in Fig. 3. These results are shown in Fig. 4. In this figure, the error bars correspond to the standard deviation of the transmission. The model for Dome C stands out as being far more stable than Chajnantor where the transmission in median stability conditions can varies by up to 20%. It is only at 200 µm, for the worse 50% stability conditions that the stability at Dome C becomes an issue.

The Antarctic plateau is therefore a potentially ideal site for submm/THz observations. The conditions are probably always ideal for observations at 450 µm and is a potential ground based site where 200 µm observations can be made paving the path for complementary observations with higher angular resolution to the Herschel Space mission.

Site testing and science qualification

Chajnantor plateau in Chile is currently presented as the best submm astronomy site on Earth. However, our analysis in Sect. 2 predicts that the French-Italian Concordia base could be a potentially remarkable observatory on Earth for submm/THz astronomy. A site testing campaign that is specifically designed for submm/THz astronomy is clearly required. The transparency (i.e. the optical depth τ ) and the stability (i.e. the sky noise σ τ ) must be measured to determine whether Dome C is a better site than Chajnantor.

The road map for site testing that we propose to follow at CEA Saclay will consist of three steps. (1) The GIVRE experiment (2007) in collaboration with IPEV and LUAN, Nice aims to determine the impact of frost and icing on hardware at the Concordia station. (2) The SUMMIT 200 µm tipper experiment (2008) in collaboration with UNSW, Sydney aims to measure τ and therefore obtain a clear estimate of the atmosphere transmission. (3) The CAMISTIC project (2009)(2010) at CEA Saclay aims to install a filled bolometer-array camera with 16x16 pixels on the IRAIT telescope [START_REF] Tosti | Proceedings of the SPIE[END_REF] at Dome C and explore the 200µm (i.e. THz) windows for ground-based observations. CAMISTIC will perform site testing on the atmospheric transmission and sky noise in the 200-µm range. Sky noise measurements will be performed through sky imaging with the whole bolometer array. On a 80-cm telescope (IRAIT), the near field is located at 1.6 km for 200 µm. This means that the overlap between the bolometer pixel beams will be greater than 50% until about 1 km, and above the near-field limit, the pixel beams completely diverge. If the water vapour cells responsible for the fluctuations in the atmosphere background power (i.e. sky noise) are located below ∼ 1 km, the pixels "see" through the same atmosphere column and sky noise. However, if these water vapour cells are above 1.6 km, then non adjacent pixels on the array "see" through different atmosphere column and can potentially provide a map of the sky noise.

In the future this site testing campaign should demonstrate whether Dome C is the best site on Earth for submm/THz astronomy and therefore open the path to large (> 10) diameter telescopes at the Concordia station (see Olmi et al., this volume).

Fig. 2 .

 2 Fig.2. Modeled atmospheric transmission for submm/mm wavelengths. Colour plots are ATM models(Pardo et al. 2001) for Chajnantor with PWV equal to 0.1 mm (green), 0.2 mm (yellow), 0.5 mm (red) and 1 mm (blue). The solid line represents the atmospheric transmission expected at Dome C for PWV=0.17 mm(Lawrence 2004).

Fig. 3 .

 3 Fig. 3. Atmospheric transmission vs. cumulative time frequency for Chajnantor (red) and Dome C (blue). Left: 450 µm. Dome C modeled transmission remains above 50% during 90% of the time. Right: 200 µm. The window opens only less than 10% of the time for a transmission above 20%.

Fig. 4 .

 4 Fig.4. Atmospheric transmission vs. cumulative frequency at Dome C (black circles) and Chajnantor (red stars). The error bars correspond to the modeled standard deviation of the transmission for the 25% and 50% stability percentiles. 25% stability means the stability level that is expected 25% of the winter time.


T Chailloux

A Capitaine

B Erable

G Pillonnet

Autonomous Sensor Node Powered by cm-scale Benthic Microbial Fuel Cell and Low-cost and Off-the-Shelf Components

Keywords: Microbial Fuel Cell, Energy harvesting, Autonomous sensor node

Microbial fuel cells (MFC's) are promising energy harvesters to constantly supply energy to sensors deployed in aquatic environments where solar, thermal and vibration sources are inadequate. In order to show the ready-to-use MFC potential as energy scavengers, this paper presents the association of a durable benthic MFC with a few dollars of commercially-available power management units (PMU's) dedicated to other kinds of harvesters. With 20cm 2 of cheap material electrodes, and experimental conditions similar to real ones, 101µW has been generated at 320mV in steady-state operation. In burst mode, the MFC can generate up to 400µW. The PMU, configured to extract the maximum available energy, provides 47µW at 3V in steady state, which would allow a wide range of environmental sensors to be powered. A sensor node, consuming 100µJ every 4s for measurement and wireless transmission of temperature, has been successfully powered by the association of our MFC and the PMU.

Introduction

Harvesting energy from the surrounding environment is an advantageous alternative to conventional batteries for powering autonomous remote sensors. Solar energy, thermal gradient and mechanical vibration are widely used as conventional energy harvesters. However, the microbial fuel cell (MFC), though less studied, is a promising technology that exploits the catalytic properties of bacteria in a few redox reactions, to convert chemical energy from sediment into electrical energy. In addition, the field of application is large regarding the wide range of organic substrates that can be used (organic rich sediment, compost, waste water) [START_REF] Kiran | Microbial Fuel Cell: technology for harvesting energy from biomass[END_REF][START_REF] Mohan | Microbial fuel cell: Critical factors regulating bio-catalyzed electrochemical process and recent advancements[END_REF]. It is also noteworthy that they can be deployed in regions where any other energy harvesters would be inappropriate (seafloors, sewage works). Finally, they can work in a wide range of operating conditions [START_REF] Jadhav | Performance of microbial fuel cell subjected to variation in pH, temperature, external lad and substrate concentration[END_REF] and for a long time [START_REF] Sevda | Shift to continuous operation of an air-cathode microbial fuel cell longrunning in fed-batch mode boosts power generation[END_REF].

The MFC is a relatively mature technology but the generated power is not directly usable to power low-power sensor nodes continuously. Typically, it generates a few microwatts per square centimeter of electrode, at only a few hundred millivolts. However, different approaches are used to overcome this problem. The first solution consists in stacking several MFCs to have a higher voltage [START_REF] Aelterman | Continuous Electricity Generation at high voltages and currents using stacked Microbial Fuel Cells[END_REF] and use a capacitor to store the energy. Another possibility consists in using just one MFC and raising its voltage with switched-mode converters, such as a capacitive converter (charge pump) [START_REF] Lee | System-On-Mud: Ultra-Low Power Oceanic Sensing Platform Powered by Small-Scale Benthic Microbial Fuel Cells[END_REF] or inductive converter (boost or flyback converter) [START_REF] Khaled | Autonomous Flyback Converter for Energy Harvesting from Microbial Fuel Cells, Energy Harvesting and Systems[END_REF][START_REF] Capitaine | Loss Analysis of Flyback in Dicontinuous Conduction Mode for sub-mW Harvesting Systems[END_REF]. Some researchers propose dedicated circuits to efficiently harvest the MFC energy [START_REF] Wang | Practical Energy Harvesting for Microbial Fuel Cells: A review[END_REF], but these circuits are not directly available for a company which would like to massively deploy this technique in a wide range of applications. The aim of this paper is to demonstrate the efficient association of a low-cost benthic MFC with a commercially-available circuit, designed for other scavenging sources, and to describe the first steps necessary to configure it in order to extract the maximum power from the MFC.

Benthic MFC

MFC elaboration

A schematic diagram of the MFC is shown in Fig. 1. Bacteria catalyze the oxidation of the organic substrate on the Anode A while the oxygen dissolved in fresh water is reduced at the Cathode C, inducing a transfer of electrons from A to C and thus electrical energy generation [START_REF] Kiran | Microbial Fuel Cell: technology for harvesting energy from biomass[END_REF]. Marine sediment was chosen as the anaerobic bacterial medium and as "fuel" (organic matter) in which a thick graphite felt [START_REF]Datasheet of MERSEN RVG[END_REF] anode is buried. The sediments were collected at 43°04'13.7"N-5°47'37.6"E, a beach near a nature conservation area île des Embiez in the Mediterranean Sea. No additional substrate (e.g. acetate) was added for the start-up phase or during steady-state operation, in order to mimic natural conditions. Based on previous work on another MFC and substrate [START_REF] Capitaine | Electrical characterization and modeling of benthic microbial fuel cells for energy harvesting[END_REF], we estimated the volume of the anode (20cm 2 x1cm) to generate around 200µW. Then, a 20cm 2 , thick graphite felt cathode was placed in artificial seawater. Electrons were collected from the electrodes by 0.75mm insulated titanium wires. Although titanium is not the best electrical conductor available, it was chosen because of its high resistance to corrosion mainly due to the oxide passivation layer formed on its surface (TiO2). It is also less rare and less expensive than platinum. We used the setup according to [START_REF] Erable | Marine floating microbial fuel cell involving aerobic biofilm on stainless steel cathodes[END_REF] as the MFC is being used for more than 4 months.

The material for each fuel cell cost only a few dollars, so many MFCs could be scattered in oceans and lakes, and a large matrix of self-sufficient sensor nodes could thus be deployed all around the world.

MFC electrical characterization

As we intend to use the MFC for long-term energy generation, it needs to be characterized in steady state, meaning that each operating point must be stable for a long time before recording the current and voltage. If the sweep is too fast, the MFC characteristic could be biased by capacitive effects [START_REF] Sevda | A comprehensive impedance journey to continuous microbial fuel cells, Bioelectrochemistry, Special Issue on[END_REF] and thus its performance over-estimated for our steady-state electrical generation. The MFC characteristics found in most of the literature are obtained with fast sweeps [START_REF] Guerrini | PTFE effect on the electrocatalysis of the oxygen reduction reaction in membraneless microbial fuel cells[END_REF] and therefore only give an order of magnitude of the power available for long-term generation.

The green curve in Fig. 2 shows a static characterization of the MFC (current IMFC v voltage UMFC=VC-VA). UMFC was set step by step, while IMFC was measured in real time. The current always starts with a peak value, and then slowly decreases. Both IMFC and UMFC were recorded when steady-state was achieved, which occurred after at least 10 minutes. The average sweep speed is 33µV/s. The overall U-I curve is thus obtained after about 3 hours. The open-circuit voltage, UMFC_OC, is 510mV, confirming the need for a harvesting interface to ultimately power sensors or actuators. In short-circuit conditions, the MFC delivers 480µA. The blue curve in Fig. 2 also shows the calculated power UMFC×IMFC v UMFC. The maximum power point (MPP) is achieved at UMFC=320mV. At this point, the harvested power is 101μW corresponding to a power density of 50.5mW/m 2 , considering a 20cm 2 anode surface. The MFC was also characterized with a voltage rate sweep of 10mV/s (300 times faster than the previous characterization) and a maximum power of 401µW was measured, underlining the over-estimation of this procedure. Although these results are below the state-of-the-art ones and below our first estimation, it is worth mentioning two points. First, it is a steady-state performance, and second, no additional substrate (e.g. acetate) was used to boost the MFC start-up or its normal operation. Therefore, the data given here are the closest to the long-term generation capabilities of MFC's in a natural environment. 3 Power Management Unit (PMU)

PMU characterization

A commercial integrated circuit for scavenging sources, like photovoltaic or thermal electric generators, has been used as a PMU [START_REF]Datasheet of Texas Instruments BQ25570 Ultra Low Power Harvester Power Management IC with Boost Charger[END_REF] and configured to meet our specifications. The role of the PMU is to ultimately interface the low voltage generated by the MFC with the energy storage intermittently supplying a sensor node. It was chosen because of its low voltage (≥100mV) and low power (≥5µW) capabilities, and because of its low leakage current. A PMU basically consists of a high efficiency boost converter to raise the voltage, extract maximum power and store energy in a storage element (capacitor and/or battery). A boost converter is an electrical device that steps up the input voltage. In addition, it can adapt its impedance to reach the maximum power extraction. Since the control logic of this converter needs a minimal voltage to operate (typically >0.5V), the circuit also needs either an external voltage source (non-autonomous solution) or an auxiliary start-up circuit to initially step the voltage up to 1.8V. This is called cold-start and this low efficiency ultra-low voltage step-up conversion is typically performed by a charge pump [START_REF] Pillonnet | Sub-threshold startup charge pump using depletion MOSFET for a low-voltage harvesting application[END_REF] or, as in the circuit used here, an unregulated boost converter.

In order to measure the efficiency of the PMU close to MFC operating points, the main boost converter has been electrically characterized. For this, two Keithley 2401 SMU's were used as the power source and power sink (load), respectively, and a Keysight E3640A voltage source to set an external reference voltage for the input voltage, the PMU deciding on the right duty cycle (Fig. 3).

Voltage and current ranges were chosen according to the MFC characteristics. Since the MFC maximum voltage is 510mV and the voltage at the MPP is 320mV, the input voltage of the PMU was set between 50mV and 500mV, with finer steps around 300mV. The current was set from 25µA to 1500µA. The output voltage was set at 3V to match a large range of sensors and actuators, especially for the sensor node used in the next section. The measurements have been done after the PMU has started, since the cold start circuit on chip requires 340 mV to start. Results are shown in Fig. 4 and match those of the PMU datasheet [START_REF]Datasheet of Texas Instruments BQ25570 Ultra Low Power Harvester Power Management IC with Boost Charger[END_REF] very well. At Uin=320mV and Iin=320µA, corresponding to the MFC MPP, the PMU efficiency is around 70%, and therefore seems suitable for our power source.

At low input power, however, the efficiency of the PMU is low because the switching losses and control logic consumption are significant compared to the input power.

For the above converter, it is interesting to note that it is best to work with an input current greater than 50µA, but increasing the input current further will not lead to any major increase in the converter efficiency, whereas increasing the voltage will.

MPP Tracking

In autonomous operation, no external reference voltage is available to set the MPP. Instead, the MPP tracking (MPPT) is based on setting the PMU input voltage to a fraction of the input voltage in open circuit. Every 16 seconds, the PMU stops its operation and the open-circuit input voltage is measured after 256ms. The fraction of the opencircuit voltage is set by a customizable resistive voltage [START_REF]Datasheet of Texas Instruments BQ25570 Ultra Low Power Harvester Power Management IC with Boost Charger[END_REF] and so the PMU does not need an external reference. However, this setup cannot be used as is because the order of magnitude of the MFC time constant is much higher than the measurement time (some ten seconds). As the measurement time for the open-circuit voltage is internally set and cannot be tuned, the open-circuit voltage cannot be measured directly. However, an adaptation of this MPPT method is still possible to meet our specifications.

Based on our observations, we will consider the MFC as a first-order system with a time constant τ. Just before the 256ms rest time, the MFC voltage is set at Un. UMFC_OC, the MFC open-circuit voltage; this varies sufficiently slowly compared to the MPPT algorithm that it can be considered constant. After resting for 256ms, the voltage will increase as a first-order response (Fig. 5). Thus, Urest, the MFC voltage after trest = 256ms, is given by ( 2).

(

) 2
The PMU then sets the next MFC voltage to a fraction of Urest measured with a resistive voltage divider (3). The MFC voltage (Un) follows a sequence defined by recursion (4) and will converge to Ulimit (5) after several iterations (Fig. 5).

(

) (4) 3
Under the assumption that the MFC time constant does not change and is accurately measured (12.740s in this case), K is constant and Ulimit can be controlled and fixed as a fraction of the open-circuit voltage by calculating the proper α, by choosing the right resistance ratio. Given the characteristics of our MFC, the MPP is achieved at 60-65% of the open-circuit voltage UMFC_OC.

If UMFC_OC varies over time, the set point will auto-adjust, so a dynamic MPPT is autonomously achieved.

𝑈 𝑟𝑒𝑠𝑡 = (𝑈 𝑀𝐹𝐶_𝑂𝐶 -𝑈 𝑛 ) (1 -𝑒𝑥𝑝 (- 𝑡 𝑟𝑒𝑠𝑡 𝜏 )) + 𝑈 𝑛 𝑈 𝑛+1 = 𝑅 2 𝑅 1 + 𝑅 2 𝑈 𝑟𝑒𝑠𝑡 = 𝛼 • 𝑈 𝑟𝑒𝑠𝑡 𝑈 𝑛+1 = 𝛼 • 𝐾 • 𝑈 𝑀𝐹𝐶_𝑂𝐶 + 𝛼 • (1 -𝐾) • 𝑈 𝑛 avec 𝐾 = 1 -𝑒𝑥𝑝 (- 𝑡 𝑟𝑒𝑠𝑡 𝜏 ) 𝑈 𝑙𝑖𝑚𝑖𝑡 = 𝛼 • 𝐾 1 + 𝛼 • 𝐾 -𝛼 𝑈 𝑀𝐹𝐶_𝑂𝐶

MFC and PMU association 4.1 Electrical characterization and optimization

The MFC was connected to the previously characterized PMU as a power source and a 3V power sink (Keithley 2401) was used as a load (Fig. 6). Equations ( 6) express this coupling. By using an external reference (supplying less than 100pW to the PMU) and setting it from 0 to 510mV, the average input voltage was also controlled, in order to change the operating point of the MFC at will. Thus, the extracted power at a specific operating point is related to the available MFC power PMFC and the PMU efficiency η given by equation ( 7).

The extracted power dissipated in the load (dashed blue curve in Fig. 7) can be compared to the raw power of the MFC (solid blue curve) shown previously in Fig. 2. The red curve represents the efficiency of the PMU. The maximum extracted power is 47µW and it is achieved at an input PMU voltage of 340mV. At this optimum, the efficiency of the converter is 57%. This global MPP resulted from a compromise between the maximum power supplied by the MFC around UMFC=320mV (Fig. 2) and the increasing efficiency with rising Uin (Fig. 4).

U limit U limit U OC time PMU input voltage t rest 16s U rest (n) U n U n+1 =αU rest (n) U rest (n+1) U n+2

Powering a sensor node

Since the association of the MFC and PMU is able to generate 47µW in steady state, we experimented on their association with a low-power sensor node, for measur- V MPP_MFC = 320mV

V MPP_MFC+PMU = 340mV ing useful data in a seafloor environment (Fig. 8). The sensor node used in our experiment [START_REF] Perez | Electret-based aeroelastic harvester and its self-starting battery-free power management circuit[END_REF] is able to sense temperature and acceleration to predict algae and seismic events, respectively. The sensor is also able to communicate with a 2.4GHz Bluetooth Low Energy wireless protocol [START_REF]Datasheet of Nordic Semiconductor nRF51[END_REF]. It is configured to wake up its operation when supplied with at least 3V, and to operate with at least 1.8V. The energy consumption of the sensor node is about 340µJ to wake-up and 100µJ for each sensing cycle (including sensing, processing and wireless data emission). Assuming that about 47µW can be extracted from an MFC the minimum period for transmitting data is around 2s.

Waking up the sensor is the most critical condition for choosing the value of the energy storage capacitor. Assuming the maximum required energy is 340µJ the wakeup voltage 3V and the minimum supply voltage 1.8V, then the capacitor value has to be greater than 118µF. For our application, the harvested energy was continuously stored in a 220µF 6.3V aluminum electrolytic capacitor [START_REF]YXJ220M5X11 Miniature Aluminum Electrolytic Capacitors[END_REF] and was intermittently extracted to power the sensor node (Fig. 9).

The sensor was configured to transmit data every 4 seconds, requiring an average power consumption of 25µW. In this configuration, the sensor node was successfully detected and paired with a smartphone and data transmitted (acceleration and temperature measurements) (Fig. 9 and Fig. 10). With an antenna emerging from the water, this cheap setup could be used on the coastline to monitor the water temperature in longterm operation.

The association of an MFC and PMU could be used with the same efficiency to power a more energy-consuming sensor. For example, a sensor requiring at most 1mJ could be used at least every 21.3s. Considering the same voltage constraints as above, the capacitor value should be higher than 354µF. Depending on the technology of the energy storage element (electrolytic capacitor, supercapacitor or lithium battery, etc., the leakage current and equivalent series resistor would be different and would therefore affect the storage efficiency.

Conclusion

In this paper, a sensor node has been successfully powered by the energy harvested from a centimeter-scale, cheap and close-to-real-conditions microbial fuel cell (MFC), using a commercially-available harvesting interface. The MFC and power management unit have been thoroughly characterized separately in order to show their compatibility with our specifications. Their association allows the optimal operating point to be chosen to dynamically maximize the overall power extraction.

The harvested energy was continuously stored in a capacitor. This energy could be used to power a sensor and intermittently transmit data using wireless communication. An adaptation of the maximum power point tracking (MPPT) of the PMU has been proposed to deal with the slow dynamic of the MFC.

Our future work will focus on rethinking the power management strategy, then optimizing the MPPT, and finally designing a customized PMU IC for sediment microbial fuel cells.

Figure 1 :

 1 Figure 1: Schematic diagram of the in-situ MFC

Figure 2 :

 2 Figure 2: Static polarization curve of the MFC measured by varying the MFC output voltage by steps at 33µV/s (green curve). Output power (blue curve)

Figure 3 :Figure 4 :

 34 Figure 3: Schematic of the experimental setup used to characterize the PMU.

Figure 5 :

 5 Figure 5: MPPT operation. Evolution of the PMU input voltage to reach the target Ulimit

Figure 7 .

 7 Figure 7. Power and efficiency curves: MFC output power (solid blue curve), PMU extracted power (dashed blue curve) and PMU efficiency (red curve).

Figure 6 :

 6 Figure 6: The experimental setup used to characterize the MFC and PMU association.

Figure 8 :Figure 10 :

 810 Figure 8: The experimental setup used to power the sensor with the MFC and PMU.

Figure 9 :

 9 Figure 9: Left: Experimental setup with the MFC, PMU, sensor node, a smartphone (BLE receptor) and oscilloscope. Top right: Microbial Fuel Cell. Bottom right: Sensor node.

Acknowledgment

We would like to thank Sebastien Boisseau, Pierre Gasnier and Jerome Willemin from CEA LETI (Grenoble, France) for their sensor node design and helpful advice for testing it. We would also like to thank Alain Bergel from Laboratoire de Génie Chimique (Toulouse, France) for his collaboration on the MFC elaboration. Finally, we would like to thank Wafa Achouak and Oulfat Amin Ali from the Laboratory of Microbial Ecology of the Rhizosphere and Extreme Environment (LEMiRE) for their help on MFC characterization.