Amar Abane

Mehammed Daoui
email: mdaouidz@yahoo.fr

Paul Muhlethaler
email: paul.muhlethaler@inria.fr

Hossam Afifi
email: hossam.afifi@telecom-sudparis.eu

A down-to-earth integration of Named Data Networking in the real-world IoT

Keywords: Named Data Networking, wireless networks, Internet of Things, IEEE802.15.4, Information Centric Networking, Precision agriculture

The IEEE802.15.4 wireless technology is one of the enablers of the Internet of Things. It allows constrained devices to communicate with a satisfactory data rate, payload size and distance range, all with reduced energy consumption. To provide IoT devices with a global Internet identity, 6LoWPAN defines the IPv6 adaptation to communicate over IEEE802.15.4. However, this integration still needs additional protocols to support other IoT requirements, which makes the IP stack in IoT devices more complex and therefore shows the limitations of the IP model to support the needs of future Internet. Named Data Networking represents an alternative that can natively support IoT constraints including mobility, security and human readable data names. This paper is a synthesis of an ongoing work that investigates the integration of NDN with IEEE802.15.4 for constrained IoT devices. The proposed design has been implemented in a real-world smart agriculture scenario, and evaluated by simulation focusing on energy consumption and network overhead in comparison to IP-based protocols.

I. INTRODUCTION

The deployment of low power wireless technologies on affordable SOMs and single board computers have fostered the emergence of billions of devices, through which people and "Things" are becoming connected over the Internet. This new trend of cyber-physical Internet is commonly designated by Internet of Things (IoT [START_REF] Yaqoob | Internet of things architecture: Recent advances, taxonomy, requirements, and open challenges[END_REF]).

IoT systems are built with battery powered devices, which are computationally limited, mobile and massively deployed. Device interconnection is achieved with low-rate wireless technologies, which allow communication with a satisfactory data rate, payload size and distance range, all with years of battery lifetime. These wireless technologies provide the best compromise for resource-constrained devices. The IEEE 802. [START_REF] Shi | On broadcast-based self-learning in named data networking[END_REF].4 is the standard on which many low-rate wireless devices are based and its wide adoption proves its suitability for the IoT. Therefore, IPv6 integration with IEEE 802.15.4, known as 6LoWPAN [START_REF]Transmission of IPv6 Packets over IEEE 802[END_REF], is designed to provide IoT devices with an IP layer-3 "identity".

Nevertheless, IPv6 integration needs additional protocols to support the IoT requirements in terms of security and mobility. The current complexity of the IP stack in IoT devices exhibits its limitation to support emerging IoT applications [START_REF] Shang | Challenges in IoT networking via TCP/IP architecture[END_REF].

While efforts like 6LoWPAN try to make the IP architecture compliant with the IoT vision, the Information Centric Networking (ICN) approach natively supports the needs of the current Internet, particularly the IoT.

One of the embodiments of ICN is Named Data Networking [START_REF] Zhang | Named Data Networking[END_REF], [START_REF] Saxena | Named data networking: A survey[END_REF]. Instead of using source and destination addresses to deliver packets, NDN communications are completely based on content names. In NDN, the pieces of data are named independently of their location and requested directly from the network. Intrinsic features come along with this principle such as: communication without establishing end-to-end connections, without name resolution, native multipath routing and in-network caching. Consequently, NDN can address the IoT requirements at the network level and natively supports application semantics, data-centric security and (consumerside) mobility.

So far, the suitability of NDN for the IoT has been investigated to some extent (Section IV), but its lightweight aspect remains to be improved in order to enable NDN in IoT applications. We observe that an NDN stack implementation in IoT devices (RIOT and Contiki) can save up to 60% of ROM and 80% of RAM compared to the RPL/6LoWPAN stack [START_REF] Baccelli | Information centric networking in the iot: Experiments with ndn in the wild[END_REF]. Moreover, a simple flooding mechanism in an NDN wireless network generates three times fewer packets than RPL routing with 6LoWPAN.

This paper draws a global picture of a real-world NDN integration in the IoT. We aim to show that providing IoT devices with a layer-3 data-centric identity can be more relevant than the current IPv6 integration in terms of overhead and energy consumption, while providing satisfactory performance. Therefore, some pragmatical NDN-specific operations are proposed to enable NDN in constrained devices over IEEE802.15.4. The NDN-802.15.4 architecture is deployed in a precision agriculture system.

In the rest of this paper, Section II presents the evolution of the Internet that has led to NDN, Section III gives some details about the NDN architecture and Section IV reports on related work that investigate NDN in the IoT. Section V describes the NDN-802.15.4 architecture. Different aspects of evaluation are reported in Section VI and, Section VII concludes the paper with some perspectives.

II. FROM IP TO NDN: A PARADIGM SHIFT

Originally, the IP protocol was designed to provide a communication mechanism between two end-points, identified by numerical addresses. This host-based model was an extension of the telephone communication model to support data exchange. Therefore, IP focuses on deliver packets between a source host and a destination host. Above this model, additional layers were developed to support flow control, endto-end reliability and user applications creating the Internet protocol stack. In the meantime, revolutionary Internet applications (e.g. www) shifted the focus from identifying hosts (IP) to identifying resources (URI) leading to the creation of the DNS system, CDN and so on. Consequently, two different namespaces are currently involved in the Internet protocol stack: IP addresses and the natural content names.

However, the emergence of the IoT puts once again the IP model to the test, and highlights its limitations to provide the "true" IoT functioning. Despite middleware and additional layers, security is focused on communication channels when the data itself needs to be secured. Most IoT devices rely on the Cloud to perform access control and security verifications while they should communicate independently and directly over the network. Moreover, IoT users are expected to request and retrieve data from anywhere, over all available network interfaces while keeping a unique identity and an optimal level of security.

In this context, the Information Centric Networking paradigm creates an alternative to support future Internet features, that has been highlighted by the IoT. ICN architectures consider the data as the first-class network entity.

Although it is an L3 protocol, NDN can run as an overlay of IP, UDP or TCP protocols. This provides an opportunity for using NDN in the current Internet infrastructure. Considering firstly such a deployment is more realistic, and provides a way to experiment NDN in a real context. However, multiple approaches can be envisioned to deploy NDN on the current network infrastructure. Studying these approaches is out the scope of this paper, we consider integrating native NDN in the edge of an IoT architecture (i.e. gateway-device) and NDN over UDP/IP in the backbone. Flexible packet format. NDN packets are encoded in the TLV (Type-Length-Value) format. Although Interest and Data packets have default and optional fields respectively, they do not have predefined packet size or field sizes. TLV encoding represents an NDN packet as a collection of sub-TLVs, without a packet header or protocol version. Each TLV block is identified as a sequence of bytes starting with a predefined marker, followed by its length and its value.

III. NDN

Natural naming. NDN contents are identified through URL-like hierarchical names. A name is formed by a sequence of components separated by slashes ("/"). For example, the name /home/room1/temperature may identify the temperature value of room 1, while /home/room1/humidity identifies the humidity value in room 1, and /home/room2/all identifies all sensor values of room 2. Applications are free to design their own naming scheme since the routers do not interpret the whole name. Moreover, a producer can add name components to the initial name in order to provide more information about the content (e.g. timestamp, sequence number, geolocation, etc.).

IV. RELATED WORK A. NDN meets the IoT world

Different IoT aspects have been investigated from the NDN perspective. In this section, we focus on propositions that provide real-world deployments.

The NDNoT project [START_REF] Bannis | Creating a secure, integrated home network of things with named data networking[END_REF] is an NDN-based platform for home automation, providing features like: service management, control access, and data collection. The hierarchical structure of NDN names is efficiently examined and signed Interests are proposed to act as device commands. NDN-BMS [START_REF] Shang | Securing building management systems using named data networking[END_REF] is a building monitoring system deployed at UCLA, that manages data publishing and consuming through an access control mechanism based on the NDN data-centric security. An expressive human-friendly naming scheme has been adopted to provide a natural way to retrieve data. However, device/gateway communications use proprietary protocols while only data publishing and access control is performed with NDN. Authors in [START_REF] Burke | Secure sensing over named data networking[END_REF] and [START_REF]Securing instrumented environments over content-centric networking: the case of lighting control and ndn[END_REF] propose a secured sensing framework for NDN-based sensors. Many sensing modes are envisaged such as push, pull and on-demand. The framework mainly addresses security issues through a trust model, a pairing protocol and an access control mechanism.

In the field of wireless sensor networks (WSNs), [START_REF] Ren | Ccn-wsn -a lightweight, flexible content-centric networking protocol for wireless sensor networks[END_REF] deals with the use of Content-Centric Networking (CCN) for WSNs. Therefore, a lightweight version of the CCN protocol has been designed to accommodate constrained device requirements and small IEEE 802.15.4 frames. In the proposed protocol, restrictions are imposed on the length of packet fields and names.

Recently, the NDN protocol stack has been ported to the RIOT platform [START_REF] Shang | The design and implementation of the ndn protocol stack for riot-os[END_REF]. The implementation provides a basic support of the IEEE 802.15.4 technology with a simple fragmentation mechanism.

In a previous contribution, we proposed a one-hop NDN communication over ZigBee [START_REF] Abane | Ndnover-zigbee: A zigbee support for named data networking[END_REF] in which the sensing features of ZigBee modules were integrated in NDN. The NDN-802.15.4 integration presented in this paper is more complete and it targets IEEE 802.15.4 with dedicated operations.

Other NDN contributions for the IoT are discussed further in [START_REF] Shang | Named data networking of things (invited paper)[END_REF].

B. Wireless forwarding with NDN

Multiple studies investigated NDN routing in wireless networks, mostly focusing on MANETs. The propositions summarized below can be seen as "flood-and-learn" mechanisms [START_REF] Shi | On broadcast-based self-learning in named data networking[END_REF]; in the sense that an Interest flooding phase is used to discover content sources, and subsequent requests are forwarded more accurately based on the learned information.

In [START_REF] Wang | Rapid traffic information dissemination using named data[END_REF], a broadcasting protocol has been designed for data dissemination in vehicular networks, in which timers were used to defer packet forwarding. During the waiting time, if a node overhears a packet with the same name, the scheduled packet is dropped, which minimizes the collisions. However, this blind-flooding mechanism causes a large overhead and does not guarantee that the best path is used to retrieve content.

More elaborate solutions have been proposed to move from a blind-flooding to a controlled-flooding. In the Listen First Broadcast Later strategy (LFBL [START_REF] Michael | Listen first, broadcast later: Topology-agnostic forwarding under high dynamics[END_REF]), each node deduces the delay time before retransmission from its eligibility to forward the Interest. A node's eligibility is based on its distance from the content source that each node maintains: the closer to the source, the shorter the waiting time. This framework however, uses a distance table and endpoint identification, which slightly evokes the host-based communication model from which NDN differs.

In [START_REF] Yu | Interest propagation in named data manets[END_REF], defer timers are replaced by a forwarding rate adjustment. Each node collects its neighborhood forwarding statistics and periodically adjusts its forwarding rate (i.e. the ratio of the packets it should forward). This mechanism makes the nodes collaboratively forward packets without superfluous traffic and no additional data structure is needed. As it is based on a purely statistical method, nodes need to first get enough information and reactively adapt to changes in the situation.

In [START_REF] Baccelli | Information centric networking in the iot: Experiments with ndn in the wild[END_REF], a realistic improvement of the blind-flooding mechanism has been implemented in a network of IoT devices, with the aim of reducing network overhead and minimum routing state. After retrieving the first Data by Interest flooding, nodes keep a temporal FIB entry in order to avoid flooding subsequent Interests. If the FIB entry for the requested data does not exist, or it was deleted, the flooding phase is performed again to discover another content source. This simple modification significantly reduces the network overhead, but improvements can still be made.

V. NDN-802.15.4 INTEGRATION

The integration of NDN with IEEE 802.15.4 aims to make wireless devices an integral part of the NDN network. Therefore, a version of the NDN module should be considered for constrained devices, while providing them with all NDN functionalities.

Figure 1 depicts a typical IoT architecture considered for the NDN-802.15.4 integration. Each Low-rate Wireless Network (LWN) is accessible with a common prefix (CP). The architecture components are described below. Name-Payload-Field balancing. So far, propositions made to support NDN in constrained environments have been based on predefined restrictions. However, excluding some fields and limiting name length arbitrarily or intuitively is not suitable to cover all applications. For example, sensors that need to name small-sized data according to the location, the timestamp, the data type and the version, require maximizing the name length at the expense of the payload. Whereas, a livestock monitoring system that needs to identify each animal individually with all its related information will need to have data named with an ID and timestamp, but a larger payload is required for the data.

To support small-sized frames, it is important to understand proportion in length between name, payload and fields in a Data packet before generating it. Therefore, we propose a Name-Payload-Field function for constrained frame length.

The following notation is adopted to describe the function:

f (p) = F -(p + d + s) (1)
To find a relation between the structure of the Data packet and its size d, we study the possible field combinations and their corresponding sizes. For each possible field combination, we calculate the Data packet size including all the necessary bytes, except Name and Content fields. After calculating the Pearson correlation coefficient for the combinations obtained, the Payload-Name relation defined in Equation ( 1) can be improved to Payload-Name-Field relation, expressed as follows:

f (p) = F -(p + (8.93 + 4.36m) + s) (2)
where, m is the number of fields that are defined in the Data packet considered. The f (p) function gives the expected size of the Name field according to the payload length and the number of optional fields. Hence, it can be used as a dynamic guider for applications and devices to set the appropriate naming scheme for data, while balancing it with payload size and packet fields.

Asymmetric packet compression.

Interest packets from the backbone can rapidly become large, due to long names and optional fields. However, we observed that corresponding data from the LWN can be retrieved without sending all Interest fields. Missing fields can be calculated based on predefined configuration, NDN packet specification and previously shared information. Following the same principle, Data packets from devices can be sent with missing fields that can be generated by the gateway. Given this, we designed a simple packet compression scheme to reduce wireless network overhead.

We adopt a packet fields classification (Table I) according to which the packets are compressed, depending on whether devices maintain more or less contextual information with the gateway.

We observe that the proposed packet compression does not always require a decompression. Indeed, the LM and NEDs perform different (de)compression operations. When a NED receives a compressed Interest, it does not need to calculate all the missing fields to generate the data or to forward the Interest. Furthermore, if a decompression is required, each field can be extracted separately. When the response Data packet reaches the LM, it decompresses it and forwards it to the backbone. This packet decoding feature combined with asymmetric (de)compression help to save memory and reduce packet replications.

From an implementation point of view, the compression process consists in omitting/updating certain bytes before the packet is sent. The sender does not need to maintain the two states of the packet (i.e. compressed and decompressed). Similarly, the receiver decompresses the packet by adding/updating certain bytes.

Regarding security, data authenticity is not compromised by packet compression. When each NED signs its data, the initial (i.e decompressed) Data packet is signed before its compression, and the decompression process by the LM adds the exact bytes needed to make the signature verification correct. When the Data signature is delegated to the LM, the Data packet is signed by the LM after the decompression.

Fragmentation. In NDN, routers need the entire Interest and Data packets to perform NDN forwarding operations. Therefore, a hop-by-hop fragmentation is the only one possible when a packet is larger than the link MTU.

Alternately, applications can use NDN segmenting and/or sequencing to transmit and retrieve large data amounts separately, and without causing extra computation or additional header in the strategy layer.

However, Data signature is mandatory in NDN and it is frequently achieved with public key encryption, making it difficult to fit a Data packet with a payload and a signature into one IEEE 802.15.4 frame. With packet compression, we expect that fragmentation will be required only when public key cryptography is used. In this case, we adopt the 3-byte fragmentation header is proposed in [START_REF] Shang | The design and implementation of the ndn protocol stack for riot-os[END_REF]. The proposed forwarding strategy (denoted iLNFS) does not use device addresses. Instead, it follows the aforesaid floodand-learn approach, and relies on broadcasts (and overhearing) to retrieve content based on names and time-deferred retransmissions. Unlike related solutions, the flooding phase in our proposal is triggered only once; when a new prefix appears in the network. As a consequence, the number of packet retransmission is reduced to the bare minimum without requiring to maintain additional information. iLNFS is based on an adaptation of the Q-learning framework which is not described here due to space limitation. However, to reduce the network overhead and keep a minimal state in NEDs, random exploration and data structure used in Q-learning are replaced by a combination of partial-learning and real-time delay refining.

Considering the Q-learning framework, each node maintains a Q-value of the corresponding content prefix and updates it after retrieving (or overhearing) a Data packet, according to the following equation:

Q x (p) = (1 -α)Q x (p) + α (r + min Q y (p)) (3)
where α is the learning rate, r is the reward 1 , Q x (p) is the Qvalue of node x for the prefix p, and min Q y (p) is the smallest Q-value recently heard by node x.

After the first discovery of the content source with an Interest flooding phase, the effective strategy starts as follows: (i) the source node responds with a Data packet carrying the initial Q-value, (ii) the first forwarder on the source-consumer path computes its Q-value, replaces the Q-value with the value obtained, and retransmits back the Data packet, (iii) each node on the path follows the same procedure until the Data packet reaches the consumer, (iv) in the vicinity of the path, nodes that overhear the Data packet can perform a passive Q-value update to learn their position relative to the data source.

Subsequent Interest forwarding is then performed by combining partial-learning and real-time delay-adjustment phases.

The forwarding decision in a relay node consists in finding the appropriate delay to wait before retransmitting the Interest.

Let Q x (p) and Q y (p) be the current Q-values for the prefix p at nodes x and y respectively. Let Φ(a) be the delay to wait before retransmission, and a = ∆ + θ. The definition of Φ will be given later.

Whenever node x receives an Interest issued (or forwarded) by node y, it computes the value ∆ = Q y (p) -Q x (p). Here, ∆ quantifies the global eligibility of node x to forward the 1 Assuming the hop-count as a metric, the reward is always equal to 1, and the Q-value of each node represents a cost to get the content, which increases as the distance to the content source increases. The Q-value of the content source is 0.

Interest. If ∆ ≥ 0 then node x can potentially forward the Interest and can evaluate θ to obtain the delay time before Interest retransmission.

θ is a statistical value used to adjust the time to wait according to the neighborhood activity.

Let N a be the neighborhood activity rate for all data names. From the perspective of a node, N a can be computed by N a = D u /I d , where D u is the number of unsolicited received Data and I d is the number of non-forwarded Interests (dropped Interests). Then, θ may be simply defined as θ = T h -N a , where T h ≤ 1 is the activity threshold above which the waiting time should be increased. For simplicity, but without losing accuracy, N a is kept between 0 and 1. Thus, if T h is strictly lower than 1 (e.g. 75%), θ can be negative. In this case, the value of ∆ is reduced, which will increase the waiting time. When no statistic is available, N a = T h.

After obtaining the appropriate value of a, the delay time is computed with the following function:

Φ(a) = M e a/2 + m (4)
Here, the waiting time is upper-bounded by (M+m) units when a = 0; m forces the forwarder to wait for a minimum period to prioritize a response Data packet if any.

During the delay-listening time, if node x detects that a forwarder z is transmitting a packet with the same name, it deduces that z is more eligible to handle the Interest and cancels its retransmission.

In order to deal with obsolete distance estimation, each node uses Interest timeout to reset the corresponding Q-value to a maximum value. After that, it will be able to update it with an accurate estimation.

VI. EVALUATION

We split the evaluation into three parts: (i) an implementation measurement achieved in a real-world IoT deployment, (ii) a local-scale evaluation of the wireless forwarding strategy, and (iii) a full-scale overhead estimation in comparison to IPv6.

A. IoT deployment

The NDN-802.15.4 architecture has been deployed in a cow health monitoring system for precision agriculture. Specifically, the system uses sensors (e.g. movement, temperature, etc.) to collect the data of each cow. The data is then analyzed to detect whether a cow is sick, or to forecast cows' activities such as heat periods to make breeding decisions more accurate. Using the data-centric NDN security, the information related to each cow is signed directly when it is collected by the sensor (i.e. NED). Hence, every individual cow has a unique identity (not an address) and it is bound to its data at network level.

The implementation is based on a Linux PC as the gateway/local-manager (LM) and Arduino DUE boards with XBee S1 modules as NEDs. A monitoring application periodically sends Interests (44 bytes) to request content produced by NEDs (Data packet of 105 bytes). Some measurements have been made and reported in Table II. In comparison, the RTT measured is below 6LoWPAN performance (RTT = 9 to 25ms [START_REF] Gardasevic | On the performance of 6lowpan through experimentation[END_REF], [START_REF] Cody-Kenny | Performance evaluation of the 6lowpan protocol on micaz and telosb motes[END_REF]). However, 6LoWPAN packets are not signed and additional layers are required to support data naming, while the measured RTT includes Data creation and signature, and Interest-Data (de)compression.

The compression ratio is

B. Wireless forwarding performance

To evaluate the wireless forwarding strategy, we implemented it in the OMNeT++ simulator 2 . We considered a configuration with one gateway and 30 end-devices moving in an area of 200x200 m 2 following the random way point model, at a speed of 1 m/s to 3 m/s. Packet sizes are the same as in the deployment. The IEEE 802.15.4 characteristics with a communication range of 35 m are considered.

Figure 3 reports the average energy consumption of enddevices in comparison to AODV, under the same radio consumption parameters. We chose AODV as a reference as it is a reactive protocol with a communication schema close to NDN.

Fig. 3: iLNFS and AODV energy consumption

For 500 requests issued by the gateway, iLNFS generated 7653±190 transmissions, while AODV generated 12000±500. This is due to the fact that iLNFS does not use route discovery, and the flooding is reduced to the bare minimum. Moreover, as no additional packet type exists in iLNFS, the low number of transmitted packets may indicate that the best paths are frequently used to retrieve content, which is not guaranteed when the forwarding relies on Interest flooding with no route discovery. The difference in the number of transmitted packets explains why iLNFS globally consumes less than AODV.

iLNFS outperforms AODV in terms of round-trip time, mainly because of the network density in which AODV is penalized by the medium access algorithm through delayed transmissions and transmission retries.

C. Full-scale overhead

We considered a full-scale topology consisting of 6 core routers, 5 LWNs and 3 groups of consumers placed over the network.

Based on the packet exchange trace obtained for 5 minutes of simulation, we calculated the overhead generated by the network according to the number of consumers, with an estimation of UDP/IPv6 overhead with packet compression (Figure 4). Considering a realistic deployment of NDN over UDP/IPv6, we observe 7% to 20% overhead reduction when using the proposed NDN integration.

D. Feature comparison

A comparison between 6LoWPAN (IP) and NDN-802.15.4 (NDN) features is given in Table III.

VII. CONCLUSION

In this paper, a key aspect of NDN for the IoT was investigated by proposing a realistic NDN-802.15.4 architecture. The main integration issues have been discussed, and the propositions were evaluated in a real-world deployment, a local-scale and full-scale simulations.

This design is a first step towards the integration of the ICN paradigm in constrained IoT environments, by giving another look at some IP-based integration techniques (e.g. 6LoWPAN). The proposed mechanisms show the flexibility of NDN for low rate lossy technologies such as the IEEE802.15.4. The NDN-802.15.4 architecture obtained aims to shape a novel and strong NDN-IoT duo.

Moreover, a lightweight NDN forwarding strategy in wireless networks was proposed. This AI-inspired mechanism provides a realistic wireless forwarding for NDN without changing its structures.

As a future work, we aim to investigate the proposed operations in depth and make an accurate performance comparison of NDN and IP architectures.

Fig. 1 :

 1 Fig. 1: IoT architecture for NDN-802.15.4 integration

•F

  : payload length of the frame considered. (e.g. F = 100 Bytes) • d: length of the Data packet with defined fields (except Name and Content fields) and all necessary Type-Length bytes • p: payload length of the Data packet. (p ≥ 0 Bytes) • s: length of the signature value in the Data packet • f (p): size of the Name field according to the variable payload (f (p) ≥ 0) Here, a Payload-Name relation can be intuitively evaluated as:

Fig. 2 :

 2 Fig. 2: NDN-802.15.4 stack, OSI model and 6LoWPAN

Fig. 4 :

 4 Fig. 4: Total overhead of NDN and UDP/IPv6 NDN generates much less additional data than UDP/IPv6 because of the NDN caching that reduces the number of packet

TABLE I :

 I Packet fields classification.

	Class	Description	Fields	Treatment
	STATIC	Shared by NEDs and the LM in the local wireless network	(part of) Name, NameComponent	Not transmitted
	INFERRED	Not exactly the same for the LM and NEDs, but can be calculated using shared context and trust conventions	SignatureType, KeyLocator	Not transmitted
	DEFAULT VALUE	Fields with a default value defined in the NDN specification, or fields that can be omitted	ContentType, FreshnessPeriod, HopLimit InterestLifetime, MustBeFresh	Not transmitted when default value
	VARIABLE	Can not be inferred and not common to LWN nodes	Nonce, Content, SignatureValue, Parameters	Always transmitted
	UNSUPPORTED	Not supported because their processing is too complex for constrained devices	Rest of the fields (e.g. ForwardingHint).	Not transmitted

  1.35, which is to say, 39 Bytes are gained by the packet compression in each Interest-Data exchange. The compression delay (CD) is 6µs as it only consists on adding/skipping bytes. The implementation of the NDN802.15.4 module occupies 6% of the total memory. It includes the strategy layer with the three NDN tables and the iLNFS operations, without signature algorithms. As an empirical comparison, some open source implementations of the IPv6 stack over IEEE802.15.4 take about 12% storage and 45% RAM in the Arduino Mega, while the NDN-802.15.4 module takes approximately 5% storage and 20% RAM.

TABLE II :

 II Deployment measurements

	Metric/Operation	Value
	RTT	72 ms
	CR	1.35
	CD	6 µs
	First (random) Interest forwarding	55µs
	Subsequent Interest forwarding	10µs
	FIB entry update	18µs

TABLE III :

 III .4 and 6LoWPAN features comparison

	Feature	NDN-802.15.4	6LoWPAN
	Fragmentation	Yes	Yes
	Packet structure	Name-Payload-Field balancing	Fixed packet format
	Compression	Packet compression	Header compression
	Mobility	Simple adaptations	Additional protocols NEMO, AdapterMIPv6, etc.
	Security	Native data-centric security	MAC and TLS security

To accommodate non-NDN terminology, in this Subsection we refer to LM and NED by gateway and end-device respectively. Likewise, Interest and Data are referred to as Request and Response respectively.


The newly discovered macrolactone mucorolactone, along with 8 known compounds, was isolated from an ethyl acetate extract of the insect-born fungus Mucor sp. All structures were elucidated using 1D and 2D NMR and MS spectroscopic experiments. Relative and absolute configurations of the original skeleton of mucorolactone was deduced from NOESY experiment, from the 13 C NMR chemical shifts calculation based on the DP4 probability method, and from the comparison of experimental and calculated electronic circular dichroism spectra.

Natural products are a historically successful option in the drug discovery and development process. They have an indispensable role to play, continuing to provide for and influence the search and development of current therapeutic drugs in spite of the reduced level of natural products based drug discovery programs. 1 The recent decade has seen a growing recognition that natural products isolated from plants and animals may sometimes be the metabolic products of co-living microbes. 2 This fact leads researchers to the exploration of underrated ecological niches to identify novel chemical entities. Consequently, natural products regulating insect-microbe interactions have emerged as a promising resource for drug discovery. 3,4 These microorganisms, either mutualists or pathogens, generate a number of secondary metabolites involved in the mechanisms of defense, protection, or virulence. 5,6 Among them, entomopathogenic microorganisms are expected to produce insecticides and antimicrobial compounds during the process of infection and proliferation. 7 In our work, we undertook the exploration of secondary metabolites of the fungus Mucor sp. isolated from the cuticle of an alive, yet infected, unidentified wasp (Vespidae) collected in French Guiana.

The structure of mucorolactone (1).

A fractionation was performed on the ethyl acetate extract of Mucor sp. SNB-VECD13A and a new compound 1 (Figure 1) was isolated, along with known dehydroabietic acid, 8 cyclo(L-Pro-L-Phe), 9 N-acetyltryptophane, 10 daidzein, 11 2-(4hydroxyphenyl)ethyl acetate, 12 phenylacetic acid, 13 ergosterol-5α,8α-peroxide, 14 and ergosta-5,7,22-trienol. 15 Known compounds were identified by the comparison of their analytical data with those from the literature, as described in the Supporting Information. 1). The presence of a pyranose moiety was confirmed by the sequence of 1 H -1 H COSY cross-peaks for H-1/H-2/H-3/H-4/H-5 and H-6 along with HMBC correlation H-1 to C-5 (Figure 2). An additional sequence of 1 The anomeric proton configuration was assigned based on H-1 chemical shift and on the coupling constant value of 7.6 Hz between H-1 and H-2. This is consistent with a βconfiguration. Based on the same principle, the vicinal coupling constant between the successive pyranose protons provided information about their relative configuration. Large J couplings observed within the sequence H-2 to H-5 confirmed the axial configuration of H-2, H-3, H-4 and H-5. These orientations were confirmed by the NOESY spectrum where correlations were observed between the anomeric proton H-1 (δ H 4.32) and H-3 and/or H-5, and between H-2 and H-4. The pyranose was therefore defined as a glucose moiety. The relative stereochemistry in the cyclohexane ring was determined as follows. First, H-8 and H-10 both have 3 small vicinal coupling constants (J ≈ 2.5 Hz and J ≈ 2.1 Hz, respectively). Since one of the H-9 protons is necessarily axial, then H-8 and H-10 must be equatorial. The axial H-9 proton at δ 2.04 could be identified due to the NOE correlation with H-11. H-12 has one large coupling with H-13 and two small couplings with H-11 and H-7. Since H-11 is axial, then H-12 has to be equatorial. This was confirmed by the NOE correlation between H-11 and H-12. Finally, the COSY experiment indicated that H-7 was coupled with H-9eq (W-coupling) and should therefore be equatorial. This assumption was reinforced by the NOE coupling between H-7 and H-13. Scheme 1. LC-MS 2 Fragmentation of 1 (up: positive mode; down: negative mode)

The only stereochemical information linking the glucose to the cyclohexane ring is an NOE correlation between H-1 and H-8. However, this was not enough to determine the relative configurations of these two moieties (Figure 3). A 13 C NMR chemical shift calculation by the DP4 probability method was performed. 16 Due to the flexibility of the compound, 13 C NMR calculations have been performed on the conformers within 1kcal.mol -1 using Gaussian 09W with the B3LYP method at the 6-311+G(d,p) level. The Boltzmann weighted chemical shifts have been compared to the experimental ones using an in house implementation of the Java source code available on the Goodman's group page (http://www-jmg.ch.cam.ac.uk/tools/nmr/DP4/). The diastereoisomer a was deduced to be correct with a 100% probability. The experimental electronic circular dichroism spectrum of 1 was recorded in t-BuOMe. It shows a broad positive absorption band centered at 220 nm. The calculated ECD spectrum of the enantiomer in Figure 1 agreed well with the experimental data (See supporting information), therefore supporting the absolute configuration in which the glucose moiety is D. The initial goal was to find new insecticides. Mucorolactone was tested on Aedes aegypti larvae and was not active. In conclusion, this letter reports the first description of mucorolactone (1), a unique macrocyclic compound isolated from a fungus of the genus Mucor. Interestingly, the right side of compound 1, from the ester group in C-19 to the pentyl side chain (C-36-C-40), seems to be formed by cyclization (formation of the C-11-C-12 bond) and oxidation of -linolenic acid to generate the functionalized cyclohexane ring. Bacteria from the Alicyclobacillus and Sulfobacillus genera are known to produce -cyclohexyl fatty acids, 17 but simple 6-membered rings in the middle of a fatty acid chain are very rare in nature. One example is the isolation of rubrenoic acids A-C from the marine bacterium Alteromonas rubra. 18 These compounds probably originate from the 7-12 cyclization of a C16 fatty acid, but the biosynthetic pathway leading to the formation of such compounds is not known. It will be interesting to further investigate the biosynthesis of mucorolactone, which can be obtained in large amounts from Mucor sp. SNB-VECD13A.

  H -1 H cross-peak H-7/H-8/H-9/H-10 and H-11 along with HMBC correlations between H-8/C-9, H-10/C-8 and C-12, H-11/C-12, and H-12/C-7 and C-10 allowed the determination of another six-membered ring linked to the pyranose moiety by C-8 as shown by the H-1/C-8 and H-8/C-1 correlations (Figure 2). Note that the unusual C-11-C-12 bond is revealed by the H-11-H-12 COSY correlation, and the correlations H-11/C-12, H-10/C-12 and H-12/C-10 in HMBC. The 1 H-1 H COSY sequence signal from H-12 to H-18 established a carbon side chain linked to C-12 and includes the two methines H-13 and H-14 in a cis double bond (J = 11.3 Hz, Figure 2). HMBC cross signals H-6/C-19 and H-18/C-19 confirmed the C-6-O-C-19 linkage demonstrating the presence of a macrolactone. The correlation of H-7 with C-20 and 1 H-1 H coupling between H-11 and H-36 allowed us to attach an alkyl side chain in C-11 and a fatty acid chain in C-7. The relative length of both side chains could not be established by NMR due to extensive overlapping of the CH 2 signals in 1 H and 13 C. In LC-ESI --MS 2 , the fragmentation of the pseudomolecular ion yielded two characteristic fragments at m/z 255.2323 ([C 16 H 31 O 2 ] -) and 471.2584 ([M-C 16 H 31 O] -). These originate from the cleavage of the C-7-O and the O-C-20 bonds, respectively (Scheme 1). In LC-ESI + -MS 2 , the [M+Na] + adduct fragmented to give a daughter ion at m/z 477.2457 ([M+Na-C 16 H 32 O 2 ] + ). All these findings are consistent with the existence of palmitate esters on C-7 and a fivecarbon side chain in C-11.

Figure 2 .

 2 Figure 2. A) COSY (bold), and key HMBC (plain arrows) correlations in compound 1; B) key NOE correlations in the glucose moiety (dashed arrows). Hydroxyl groups in positions 2-4 have not been represented for clarity; C) key NOE correlations in the cyclohexane ring (dashed arrows).

Figure 3 .

 3 Figure 3. Both possible diastereoisomers of 1 (R = pentyl).

Table 1 . 1 H and 13 C NMR Data of Compound 1 a 1

 1 Measured at 500 MHz for1 H and 125 MHz for13 C, in methanold4 Compound 1 was obtained as a yellow oil. It has the molecular formula C 40 H 70 O 10 , as deduced from the HR-ESI + -MS molecular ion sodium adduct at m/z 733.4881 [M + Na] + (calcd for C 40 H 70 O 10 Na, 733.4879), corresponding to 6 degrees of unsaturation. The examination of the 1 H,13 C, and HSQC spectra of 1 allowed the identification of two triplet methyls at δ H 0.89 and 0.90 and thirteen methines [δ H 6.08, 5.46, 5.33, 4.44, 4.32, 4.13, 3.95, 3.73, 3.51, 3.34, 3.33, 3.15 and 2.85] among which two were olefinic protons (δ H 6.08, H-13 and δ H 5.46, H-14). Several methylenes were identified under a broad signal at δ H 1.30. The 13 C NMR spectrum of 1 showed the presence of two quaternary carbons corresponding to ester groups at δ C 175.7 and 174.0 and nine oxygenated carbons between δ C 63.1 and 106.1 (Table

	no.	δ H , mult (J in Hz)	δ C
	1	4.32, d (7.6)	106.1
	2	3.15, dd (9.4, 7.6)	75.5
	3	3.329, br t (9.3)	77.5
	4	3.51, br t (9.3)	70.6
	5	3.332, br dt (9.6, 2.0)	74.6
	6	a: 4.13, dd (11.7, 1.6)	63.2
		b: 4.44, dd (11.7, 2.5)
	7	5.33, br t (2.7)	76.0
	8	3.73, br q (2.5)	77.3
	9	ax: 2.04, dt (15.2, 3.8)	34.5
		eq: 2.12, br d (15.2)
	10	3.95, br q (2.1)	70.1
	11	1.71, m	38.6
	12	2.85, br d (10.1)	39.1
	13	6.08, br t (10.7)	131.1
	14	5.46, br td (11.2, 5.2)	130.8
	15	a: 1.76, m	27.6
		b: 2.43, m
	16	1.50, m	31.0
	17	a: 1.64, m	26.5
		b: 1.84, m
	18	a: 2.27, ddd (13.8, 8.8, 5.2)	36.6
		b: 2.41, m
	19		175.6
	20		174.0
	21	a: 2.29, dt (15.0, 7.3)	35.6
		b: 2.33, dt (15.0, 7.3)
	22	1.61, m	26.5
	23-34	1.21-1.35, m	23.8 to 33.3
	35/40	0.89/0.90, t (7.0)	14.6
	36	1.42, m	31.3
	37-39	1.21-1.35, m	23.8 to 33.3

a

ANR-10-LABX-25-01). The authors are very grateful to C. Gourmel (BioSavane association) and C. Duplais (CNRS) for collecting the infected insect. The authors also acknowledge Marcelino Suzuki (Sorbonne Université) and Mehdi Beniddir (Université Paris Sud) for constructive discussions.

ASSOCIATED CONTENT Supporting Information

The Supporting Information is available free of charge on the ACS Publications website. Detailed experimental procedures, NMR and MS data (PDF)

AUTHOR INFORMATION Corresponding Author

*Tel: +33 169 82 36 79. E-mail: veronique.eparvier@cnrs.fr *Tel: +33 4 30 19 24 76. E-mail: didier.stien@cnrs.fr

Author Contributions

‖These authors contributed equally.

ACKNOWLEDGMENT

This work benefitted from an "Investissement d'Avenir" grant managed by the Agence Nationale de la Recherche (CEBA, ref


Amar Abane
email: amar.abane@nist.gov

Paul Mühlethaler

Samia Bouzefrane
email: samia.bouzefrane@lecnam.net

Paul Muhlethaler
email: paul.muhlethaler@inria.fr

Abdella Battou
email: abdella.battou@nist.gov

Modeling and Improving Named Data Networking over IEEE 802.15.4

Keywords: NDN, ICN, IoT, IEEE 802.15.4, Mathematical Model, CSMA, Low-power wireless

I. INTRODUCTION

Internet of Things (IoT) systems are built with battery powered devices with limited computation and memory capacity. Device interconnection is achieved with low-rate wireless technologies which allow communication with a satisfactory data rate, payload size and distance range, all with years of battery lifetime. One of these technologies, the IEEE 802.15.4 [START_REF]Ieee standard for local and metropolitan area networks-part 15.4: Low-rate wireless personal area networks (lr-wpans)[END_REF], greatly contributes to make IoT possible. To support IoT solutions with IP, Internet Engineering Task Force (IETF) efforts have resulted in extensions to TCP/IP protocols and the appearance of various other protocols acting like middleware between the application layer and the network layer. Nonetheless, IP still suffers from severe limitations that are becoming difficult to address as they are related to the host-based networking paradigm itself, as studied in [START_REF] Shang | Challenges in IoT networking via TCP/IP architecture[END_REF]. In practice, most IP solutions support the IoT at application level using the Representational State Transfer (REST) architecture, which indicates that the TCP/IP stack has reached its limit to support these new requirements.

Unlike host-based networking, Named Data Networking (NDN) [START_REF] Zhang | Named Data Networking[END_REF] operates with named content. In NDN, every piece of content is identified by a unique name which applications use to request data. Content names are independent from host location; that is, each content item keeps the same name everywhere at producers, caches and consumers. This feature is combined with cryptographic operations to provide self-secured reusable packets and enable in-network caching, since a packet is independent from its source and destination hosts. NDN is an evolved L3 protocol, as it includes some of the relevant operations traditionally provided by higher network layers such as security and flow control. With these features, NDN can match most IoT applications that focus on the content regardless of where it is located or how it is transported. Current efforts regarding NDN for the IoT consist in supporting applications in a simpler, more efficient, and more elegant way [START_REF] Shang | Named Data Networking of Things[END_REF]. Lightweight design in constrained wireless networks can be considered as a major step towards enabling NDN in low-end IoT. However, in NDN's journey to real-world IoT deployments, we believe that adaptations of current IoT-related technologies will be required. One of these adaptations concerns link-layer technologies, and particularly the 802.15.4 as investigated in this paper.

In this context, we first model a broadcast-based NDN forwarding strategy for wireless constrained networks. The model estimates the average number of frames transmitted per request (i.e., Interest-Data exchange) and the mean roundtrip time (RTT) under content popularity considerations. After that, based on mathematical and experimental observations, we consider an adaptation of the Carrier-Sense Multiple Access (CSMA) algorithm to achieve a trade-off between the Interest satisfaction, the number of transmissions, and the round-trip time. The designed adaptation, Named-Data CSMA (ND-CSMA), is derived from the CSMA algorithm to provide a better support for the broadcast-based forwarding approach.

The rest of the paper is organized as follows. Section II gives an overview of basic wireless forwarding in NDN. In Section III, the NDN wireless forwarding model is formulated and evaluated. Section IV describes and evaluates the proposed ND-CSMA scheme. Section V briefly reports on some Information Centric Networking (ICN) and NDN models formulated so far. Section VI concludes the paper with some future perspectives.

II. WIRELESS NAMED DATA NETWORKING

NDN packets do not carry source or destination addresses; packets are forwarded based on their names. Each node maintains two data structures: the Forwarding Information Base (FIB) and the Pending Interest Table (PIT). Optionally, a Content Store (CS) is used to provide in-network caching. In wireless networks, using host addresses to forward packets reduces the data dissemination potential of NDN and limits its benefits. Moreover, mapping names to addresses requires transmission overhead and more memory to maintain routes. Therefore, wireless forwarding strategies commonly use broadcast to forward packets without maintaining FIB information. A simple broadcast-based forwarding strategy is the Controlled Flooding (CF) [START_REF] Wang | Rapid traffic information dissemination using named data[END_REF]. CF is lightweight and efficient in finding requested content, even under node mobility and intermittent connectivity. CF operates as follows. The communication is initiated by the consumer that requests a content item by sending an Interest. Upon receiving the Interest, an intermediate node first checks if the requested Data locally exists in its CS. If a corresponding Data is found, it is sent back as a response without forwarding the Interest any further. When no matching data is found locally, the router checks the PIT; if an Interest for the same content is already pending, the new Interest is not forwarded. The Interest is forwarded if no similar Interest is already in the PIT. In this case, to avoid redundancy relay-nodes exploit broadcast communications to overhear packets and possibly cancel their transmissions. To do so, every relay-node defers a packet transmission with a random delay during which it keeps listening on the shared wireless medium. While waiting, if the node overhears a packet (i.e., Interest or Data) with the same name, it cancels its transmission. In practice, Interest and Data transmissions are deferred for ∆ I and ∆ D periods of time respectively. Both ∆ I and ∆ D are computed based on an interval, defer window (dw), from which an integer value is randomly chosen to generate the waiting delays as follows [START_REF] Amadeo | Forwarding strategies in named data wireless ad hoc networks: Design and evaluation[END_REF]:

∆ D =rand[0,dw-1]×Def erSlotT ime , (1)
∆ I =rand[dw,2dw]×Def erSlotT ime , (2)
where Def erSlotT ime is a short period of time.

Here, ∆ I and ∆ D are selected in disjoint intervals with ∆ I > ∆ D to give higher priority to Data packet transmissions.

Once the Interest is forwarded, the relay-node records it in the PIT. When the Interest reaches the content producer or an intermediate cache node, a Data packet containing the requested content is sent back. When the Data reaches a relaynode, it is forwarded if a corresponding Interest exists in the PIT. After that, the router discards the entry from the PIT, and stores a copy of the Data in its CS. The forwarded Data follows the reverse path of the Interest(s) hop-by-hop until it reaches the consumer(s). If a node receives a Data without a matching entry in the PIT, the packet is dropped.

Note that the CF strategy is mainly used with IEEE 802.11 technologies as it requires quite a high bandwidth to achieve good performance. However, CF can be envisioned over 802.15.4 when extremely low latency is not required. Despite deferring transmissions, flooding packets still causes high overhead and packet redundancy. In order to study the applicability of CF over constrained wireless networks, in the next section, we formulate an analytical model of CF considering content popularity.

III. CONTROLLED FLOODING MODEL A. Assumptions and Notation

We consider an IoT deployment with consumer applications requesting content produced by wireless devices. Each IoT device is provided with a single 802.15.4 interface and consumers request data through a gateway. The model assumes ideal physical-layer conditions. The network topology is considered as a full binary tree of depth N , in which the root and the leaves represent the gateway and the enddevices respectively, and the other nodes represent the relaynodes. Nodes are fixed, sibling nodes can overhear each other, but for the sake of simplicity we assume that no packet is transmitted between them. Consequently, only one path is possible between the gateway and each content producer. Relay-nodes (including the gateway) at the same level have caches of the same size. This means that the cache size is larger in the nodes closer to the gateway, and the gateway has the largest cache size.

The modeled metrics are the following: (i) Cost-per-request (CPR). The number of packets transmitted in the network to retrieve some content requested from the gateway. (ii) Roundtrip time per request (RPR). The mean delay time (in ms) measured by the gateway from sending an Interest to receiving a matching Data.

B. Content Popularity

Each relay-node in the network has a cache managed with the Least Recently Used (LRU) replacement policy. We consider a set of M content items equally divided into K classes, each one containing m = M/K content items. Each class represents a different popularity to be requested with probability q k , k = 1, 2, ..., K.

The content requested in our scenario can be considered as Web content which usually follows Zipf distribution [START_REF] Breslau | Web caching and zipf-like distributions: evidence and implications[END_REF]. Hence, to model the popularity of content classes we use a Zipf distribution,

q k = c/k α with α > 1 and c = 1/ K k=1 1/k α .
As content in the modeled application consists of a small amount of data, we consider that each content item is transmitted in one Data packet. We assume that each end-device produces the same number of content items of each class, and an item can not be produced by two different devices. The Interest arrival process is modeled through a Markov Modulated Rate Process (MMRP) of intensity λ. Interests for content in class k are generated according to a Poisson process of intensity λ k = λq k , and the requested content within the class is uniformly chosen among the m different content items in the given class. That is, a given item in class k is requested with probability q k /m. The notations and their meanings are summarized in Table I.

C. Model Formulation

We start by defining p k , p k (i) and p t . According to the topological and content production assumptions, there is no data duplication among caches at the same level. This allows us to consider all the caches at each level i simply as one cache. Given this, at the first level (i.e., gateway), the stationary miss popularity for chunks of class k, p k is defined and proven in [START_REF] Carofiglio | Modeling data transfer in content-centric networking[END_REF] as follows:

p k ≡p k (1)≈exp -λ m q k gx α (3)
for relatively large x, where 1/g = λcσ α m α-1 Γ(1 -1 α ) α . Considering a binary tree with N levels and an MMRP content request process with rate λ(i), under the popularity distribution given above, the miss probability at level i ∈ [2, N ) is also defined and proven in [START_REF] Carofiglio | Modeling data transfer in content-centric networking[END_REF] as follows:

log p k (i)=log p k (1) i-1 l=1 p k (l) . (4)
For more details on Equations 3 and 4, including proof and discussions, readers may refer to [START_REF] Carofiglio | Modeling data transfer in content-centric networking[END_REF]. When a cache miss occurs at two sibling nodes, they will both try to forward the Interest after a random delay, as described in Section II. Given the delays computed in Equation 1, the same Interest may be forwarded by both nodes if they choose random numbers with a difference smaller than s = r I /τ . Hence, the probability that two sibling nodes transmit the same Interest is equivalent to the probability that two random numbers chosen from the interval of length S = dw + 1 have a difference smaller than s. This can be formulated as follows:

pt=1-( S-2s S ) 2 +2 s-1 i=0 ( S-(i+s) S 2
) .

(

) 5
Here, we can define the CPR for retrieving a class k content item as follows:

CP R k = N i=1 ((1-pk(i)) i-1 j=1 p k (j))×(2(i-1)+ i-1 l=2 ptC k (l)) , (6)
where:

C k (l)=1+p f ((N-l-1)+ N -1 n=l+1 ptC k (n)) . (7)
Note that Equation 6 models the CPR only for the requests that have been satisfied. That is, p k (N ) = 0. Equation 6 is obtained based on the following approach. As the content can be found at any level from 1 to N , the cost is defined as a weighted sum of the transmitted packets associated to each level i. The weights correspond to the cache hit probability (1 -p k (i)) at level i given that a cache miss occurred at all the previous levels.

For every possible level i, the number of packets is composed of two parts: 2(i -1) corresponds to the number of packets transmitted along the path from the gateway to the level-i device, plus the number of packets transmitted if the sibling of each previous node (from level 2 to i -1) has also transmitted the Interest, which has a probability p t of occurring for each pair of siblings.

Here, Equation 7 assumes that when the brother of a node (at level l) transmits an Interest, the cost can be recursively computed using the same approach as Equation 6 in its subtree (from level l + 1 to N ). The only difference is that, on this side of the network, we directly consider the path from level l to the leaf level N , since the requested content has already been found elsewhere and there is no data duplication. However, in each sub-tree, the first Interest is always transmitted, but the number of transmissions recursively computed is subject to the probability that no collision occurs between the first sibling nodes of the sub-tree (p f ).

Following the same approach, we define the mean RPR for a class k content item as follows:

RP R k = N i=1 ((1-pk(i)) i-1 j=1 p k (i))Ri , (8)
where

R i = (i -1)(r I + r D + δ I + δ D ).
Here, R i is obtained by multiplying the number of hops (i -1) for level i by the total delay needed to send an Interest and get Data; which includes waiting delays (δ I + δ D ) and time-on-air (r I + r D ). When two sibling nodes delay their transmissions, the node with the shortest delay will transmit the packet first. Furthermore, the round-trip delay measured by the consumer (e.g., gateway) will be affected by the shortest waiting delay computed at each level. Hence, the global estimation of δ I and δ D is not the half way between the lowest and the highest values (e.g., dw/2τ ). To approximate the values of δ I and δ D , we consider the mean of the lowest half of [0, dw -1] and [dw, 2dw] intervals respectively. This gives us δ I = ((3dw)/4)τ and δ D = ((dw -1)/4)τ .

D. Model Evaluation

We assess the accuracy of our model using the CF strategy provided by the NDN simulation framework for OMNeT++ [START_REF] Abane | Towards evaluating named data networking for the iot: A framework for omnet++[END_REF]. We consider a tree of depth N = 4. The gateway requests content from a total of M = 3000 items, distributed in K = 50 classes of decreasing popularity, each one with m = 60 items. The request rate at the gateway is λ = 1 request/s. We set up a cache of size x = 300 packets at each level of the tree. Different popularity distributions have been simulated with α ∈ {1.5, 2, 2.5}. Each simulation result corresponds to a run of 10 hours. Each Interest packet has a size of 30 bytes and each Data packet 90 bytes. Preliminary simulations have been used to set optimal values at dw = 127 and τ = 0.032 µs. We also measured r I = 1.36ms, r D = 3r I and p f = 0.8 with preliminary simulations.

First, the Interest satisfaction rate is reported in Table II. We observe that dw = 127 always achieves better Interest satisfaction rate than 255. The reason is that 127 is low enough to make relay nodes transmit more packets and explore the network without being too low to create a lot of collisions. However, the remaining results show that this Interest satisfaction rate is achieved at the cost of much more transmissions than dw = 255. Figures 1a,1b, and 1c show the CPR according to content popularity for α = 1.5, 2 and 2.5 respectively. According to the results, the value of α has an impact on the efficiency of NDN. In fact, small values of α reduce popularity difference between classes, which introduces more diversity in the requests and thus increases the cache miss rate and CPR.

The model is also affected by α but can accurately predict the CPR according to popularity classes. The highest discrepancies between the model and simulations are observed for higher values of α. The reason is that a cache miss is more likely to occur when α is higher, which leads to more transmissions. Since dw = 127 is not high enough to avoid all redundancies, the behavior of the nodes becomes more dependent on the link-layer, which is not included in the model.

As a reference, we represent the CPR for a perfect-unicast scenario, which refers to the CPR expected in the tree if a host-based routing protocol is used instead of NDN. We note that NDN with CF outperforms perfect-unicast concerning the most popular content. This shows that transmission overhead induced by broadcast can be attenuated by small caches in the presence of popular content. Moreover, this attenuation can outperform the unicast communication pattern, which is here theoretical as it does not include route discovery/maintenance cost.

Figures 1d,1e, and 1f report on the mean RPR for α = 1.5, 2 and 2.5 respectively. The same type of observations can be made as for CPR, but a greater dissimilarity is observed between the model and simulations. The reasons are the same as for CPR, with an additional fact related to medium access time. As dw is not high enough to avoid redundant packet transmissions, the link-layer has to resolve more medium access contentions, leading to less accuracy in our model. This can be confirmed by observing raw simulation results (i.e., blue dots) which present higher scatter as α gets higher.

Figures 1g and1h show the CPR and RPR respectively for α = 2 with dw = 255. We observe that CPR becomes better when a higher value of dw is used. Compared to dw = 127, up to two transmissions per request are saved for the least popular content when using dw = 255. This makes NDN even more efficient than the host-based (unicast) approach. However, this comes at the cost of a higher RPR since waiting delays also increase when dw is higher. With dw = 255, an increase of 15 ms of round-trip delay per request is observed for the least popular content then whith dw = 127.

Overall, we find that a trade-off between cost and roundtrip delays is difficult to achieve with the CF mechanism. On the one hand, trying to reduce waiting delays by reducing dw increases the number of transmissions and collisions as nodes do not have enough time to listen to each other. On the other hand, reducing cost with higher values of dw will induce higher waiting delays. Moreover, nodes are still listening to transmissions when waiting, which is not helpful for energy consumption.

An ideal improvement one may look for is to reduce the round-trip time and energy consumption by eliminating waiting delays while keeping the lowest number of frame transmissions. Since eliminating waiting delays will significantly impact the CPR, we have to question whether a tradeoff theoretically exists that may achieve reasonable CPR, low RTT and a reasonable Interest satisfaction rate. According to the CF strategy, the tree is explored depending on whether both sibling-nodes forward the Interest or only one of them does. In the model evaluation, we find that the best performance for CF is achieved with dw = 127. Let the corresponding forwarding probability for each sibling-node pair be p * t . Then, we can easily observe that there is no value of p t lower than p * t that can achieve the same or better satisfaction rate, which is confirmed by Table II. That is, a compromise at L3 level that achieves optimal performance is not possible in our configuration.

We believe that this is due to the fact that in the forwarding decision of a node, only the sibling-node is involved. Therefore, by shifting the transmission decision to the L2 level (with some modifications) instead of using deferred transmissions, one may expect better performances since the CSMA algorithm natively considers multiple-access contention. The design and evaluation of our L2-based approach are presented in the next section.

IV. A NAMED-DATA CSMA FOR IEEE 802.15.4

To eliminate waiting delays without increasing unnecessary transmissions, we replace the waiting delays by a prioritybased CSMA scheme designed for NDN, as described below.

A. Named-Data CSMA Design

The unslotted CSMA algorithm works with a set of default parameters, and a node maintains two values when transmitting a frame: Number of Back-offs (NB) is the number of access attempts for the current transmission. Back-off Exponent (BE) is used to compute the random back-off period to wait before attempting to assess the channel.

In legacy CSMA, all nodes access the shared channel with a fair chance. Priority-based CSMA [START_REF] Zhao | Priority-based ieee 802[END_REF] uses the difference in traffic type to introduce differentiated channel access for nodes. Therefore, the priority-based CSMA mechanism is designed to make nodes with high priority traffic have a greater chance of accessing the channel. In ND-CSMA, the frames are classified into two priority classes: (i) frames that contain a

B. Evaluation

To evaluate the ND-CSMA scheme, we simulate three scenarios: (i) CF. The CF strategy with the legacy CSMA. (ii) 1 maxCSMABackoffs and aMaxBE as defined in the standard. BF. The CF strategy without waiting delays, using the legacy CSMA. (iii) ND-CSMA-x. The CF strategy without waiting delays, using ND-CSMA with th = x.

All the scenarios are simulated the same parameters (i.e., N, M, K, m, x) as those used in Section III. We set α = 2.0. For the CF strategy, we use dw = 127 and τ = 0.032 µs. We measure the following metrics: (i) RPR. The mean time needed for the gateway to retrieve a content item from a device. (ii) Transmitted frames. The total number of frames successfully transmitted. (iii) Interest satisfaction rate. This corresponds to the number of Data received by the gateway over the number of Interests it sent. (iv) Mean backoff time. The average time the nodes spent in back-off to access the wireless medium.

Figure 2 shows the results obtained. We observe that ND-CSMA with th = 1 achieves the lowest RPR compared to scenarios (i) and (ii). The mean back-off time with ND-CSMA-1 is the smallest among the evaluated scenarios, while (ii) achieves the highest back-off time due to the large number of forwarding decisions generated after eliminating waiting delays. That means, legacy CSMA without delays has to resolve medium access contention with more back-off periods Fig. 2: ND-CSMA evaluation whereas ND-CSMA has the possibility to cancel some Interest transmissions when the channel is busy rather than waiting for other back-off periods. For this reason, the RPR achieved by scenario (ii) is slightly higher than ND-CSMA. CF also is capable of canceling scheduled transmissions using deferred transmissions. However, it achieves that with a wait-and-listen mechanism which induces higher round-trip delays, and a relatively high back-off time is required when the chosen random delays are not different enough.

Moreover, the results show that forwarding with ND-CSMA-1 can ensure necessary packet transmissions while keeping the total cost at a minimum compared the two other schemes. The Interest satisfaction rate is quite similar for all the approaches, which indicates that ND-CSMA, even with low th does not reduce the efficiency of Interest flooding/broadcast. Furthermore, simulations with small caches (e.g., tens of packets) achieved approximately the same results, but with a slightly lower satisfaction rate for ND-CSMA.

Overall, ND-CSMA-1 seems to be the best compromise for the measured metrics. To return to our theoretical expectations, the results confirm that a link-layer adaptation is able to keep the benefits of a broadcast-based forwarding strategy in terms of satisfaction rate, while reducing medium access contention and the number of transmissions; it then achieves the trade-off we were looking for.

V. RELATED WORK

Most of the models on ICN/NDN are conducted exclusively around caching, such as cache deployment, cache decision and cache replacement. Other studies are devoted to ICN/NDN transport and routing performance, often with comparison to TCP/IP, such as in [START_REF] Carofiglio | From content delivery today to information centric networking[END_REF]. However, very few analytical models for ICN/NDN consider modeling networks of caches and the interaction between caching and transport; those models consider traditional wired networks [START_REF] Ren | Modeling content transfer performance in information-centric networking[END_REF]. To the best of our knowledge, no model has been formulated on NDN in wireless networks with cache consideration, whether for constrained or traditional wireless networks. We also believe that no previous proposals have been made to adapt the CSMA scheme of the 802.15.4 link-layer to improve NDN wireless forwarding.

VI. CONCLUSION AND FUTURE WORK

In this paper, we modeled a simple broadcast-based NDN wireless forwarding strategy. One objective of the model is to show that caching can attenuate the number of transmissions generated by broadcast to achieve a reasonable overhead while keeping the data dissemination power of NDN. Based on a simple modification of the 802.15.4 MAC layer, preliminary results shed light on the necessity of rethinking typical linklayer schemes for ICN/NDN such as the CSMA algorithm. As future work, we aim to explore more complex CSMA adaptations for lightweight forwarding to take the most of NDN and design a general-purpose Named-Data CSMA.

2 Fig. 1 :

 21 Fig. 1: Model evaluation results

TABLE I :

 I Model variables

	Notation	Meaning
	p k , p k (i)	Cache miss probability for class k content at the gateway, at level i > 1.
	pt	Probability that two sibling nodes transmit the same Interest given that a cache miss has occurred in both nodes.
	N	Tree depth.
	K	Number of popularity classes.
	M	Number of total content items (m = M/K in each class k).
	x	Cache size in each level in number of chunks.
	λ, λ(i)	Content request rate at the gateway, at level i > 1.
	λ k	Content request rate at the gateway for class k.
	σ	Average content size in number of chunks.
	q k , q k (i)	Content popularity distribution of requests at the gateway, at level i.
	dw	Defer window.
	r I	Time needed to send an Interest over one hop (excluding waiting delays).
	r D	Time needed to send a Data over one hop (excluding waiting delays).
	τ	Defer slot-time.
	p f	Probability that the link-layer avoids a collision given that two nodes transmitted an Interest.

TABLE II :

 II Interest satisfaction rate

		dw = 127	dw = 255
	α = 1.5	87.8%	84.1%
	α = 2.0	95.1%	93.2%
	α = 2.5	98.2%	97.4%


Pavel Müller

Petr Klán
email: klan@sci.muni.cz

Vladimír Církva

The electrodeless discharge lamp: a prospective tool for photochemistry Part 4. Temperature-and envelope material-dependent emission characteristics

Keywords: Photochemistry, Microwave, Electrodeless discharge lamp, Emission, Spectra

This work extends our previous research on an original photochemical reactor-the electrodeless discharge lamp (EDL) inside a reaction mixture that generates ultraviolet radiation in the microwave (MW) field. This arrangement was found to be a straightforward solution for homogeneous as well as heterogeneous photochemical experiments that need to be carried out at higher temperatures. Here, we report the emission characteristics (250-600 nm) of EDL as a function of temperature, MW output power of the reactor, EDL envelope material, and properties of solvents used in photochemical reactions. Relative intensities of the individual emission peaks were found to be largely dependent on temperature (in the region of 35-174 • C): the short-wavelength bands (particularly the 254 nm peak) were suppressed with increasing temperature. Solvents absorbing MW significantly reduced the EDL emission intensity. It is concluded that the right choice of EDL envelope material and reaction conditions is essential for an efficient course of a photochemical process in this experimental arrangement.

Introduction

There have been attempts to affect photochemical reactions by other sources of non-classical activation, such as ultrasound [1][2][3] or magnetic field [4,5]. Photochemistry in the microwave (MW) field [START_REF] Klán | Microwave photochemistry[END_REF] presents a combined chemical activation by two distinctive kinds of electromagnetic radiation. Energy of MW radiation (E = 0.4-40 J mol -1 at ν = 1-100 GHz) is considerably lower than that of UV-Vis radiation (E = 600-170 kJ mol -1 at λ = 200-700 nm), thus insufficient to disrupt bonds of common organic molecules. Microwave heating is not identical to classical external heating, at least at the molecular level [START_REF] Abramovitch | [END_REF][8][9][10]. Molecules with a permanent (or induced) dipole respond to an electromagnetic field by rotating, which results in friction with neighboring molecules (thereby in heat). There are some additional (secondary) effects of microwaves, including ionic conduction (ionic migration in the presence of an electric field) or spin alignment. Therefore, MW effects on photochemical reactions are expected to be diverse.

The objective of photochemistry in the microwave field is frequently, but not irreplaceably, connected to the electrodeless discharge lamp (EDL), which generates UV radiation when placed into the MW field. We have reported on an original photochemical reactor that takes advantage of an EDL inside a reaction mixture (Fig. 1) [11][12][13]. Such an arrangement was proposed for the first time by Den Besten and Tracy [14], and later applied by Církva and Hájek [15] in experiments using a modified microwave oven. Photochemical applications of a microwave-powered light source in photochemistry were recently described in several articles [16][17][18][19][20][21][22]. Knowledge of spectral characteristics of EDL is clearly essential for planning the photochemical experiment. The right choice of a filling and envelope material, glass, and even temperature can dramatically modify the emission spectrum. In the present study, we describe the influence of the EDL properties, the MW output power, and the reaction conditions on the EDL spectral characteristics.

Experimental

Equipment

The spectral measurements were accomplished in a modified MW oven Whirlpool M401 (900 W), operating at 2450 MHz frequency and described elsewhere [12], which had a window for UV radiation coming from EDL to a spectrometer. Its power was adjusted to the maximum in order to guarantee a continual MW irradiation. Every liquid is immediately boiling since EDL produces a considerable amount of IR radiation. The oven contained an external reflux condenser and a cooling glass spiral for removing redundant MW energy, thus preventing the magnetron from destruction by overheating [13,17]. Some experiments were carried out in a MW instrument Synthewave 402 (Prolabo, equipped with an IR pyrometer, a quartz reaction vessel, and an external spiral reflux condenser). The limit on the safe stray leakage of microwave power density was kept below 5 mW cm -2 at 2450 MHz measured in the 50 mm distance from the equipment. The equipment was checked for leaks especially around the modified area [13].

Electrodeless discharge mercury lamps were manufactured in Teslamp (Prague, Czech Republic). The lamps were made of a 9 or 14 mm quartz or Pyrex tubing (of approximately 1 mm thick glass) of the 13-37 mm length, filled with mercury and argon, and sealed under 2.7 kPa vacuum [12]. Pyrex absorbs most of the UV radiation below 290 nm. Gas chromatography was accomplished on a Shimadzu GC-17A apparatus. The UV-Vis spectra of all chemicals were measured on a Shimadzu UV-1601 spectrophotometer.

Chemicals

Methanol (pure, ML Chemica Co.) was used as received. n-Pentane (pure, Lachema Co.) was further purified by distillation and n-hexane (SupraSolv, Merck) was used as received. n-Heptane (pure, Lachema Co.) was washed with concentrated sulfuric acid and distilled; n-decane (pure, Reachim) was washed with concentrated sulfuric acid and purified through an activated silica-gel column. The quality of all alkanes was checked by the UV-Vis spectrophotometry and GC. The solvent absorbance values (versus air) were negligible throughout the tested range of wavelengths (250-500 nm).

Spectral measurements

A typical experimental system consisted of a quartz vessel containing a liquid and EDL, equipped with a reflux condenser, and placed to a microwave oven. The AVS-S2000 spectrometer with an optic fiber probe served for measuring and evaluating the emission spectra using the software package AvaSoft (Avantes BV). The spectrum was recorded after the light source was stabilized. The effect of the output power of the MW reactor was investigated in the Synthewave 402 MW instrument because, unlike domestic MW ovens, its power (30-300 W) can be adjusted continuously.

Results and discussion

The electrodeless discharge lamp is a glass tube filled with an excitable substance and sealed under a reduced pressure or a noble gas. A high frequency electromagnetic field (radio frequency or MW, 300-3000 MHz) can trigger gas discharge causing the emission of electromagnetic radiation. This phenomenon has been studied for many years and was already well understood in the 1960s [START_REF] Brown | Introduction to Electrical Discharges in Gases[END_REF]. EDL is usually characterized by a higher emission intensity than that of the hollow cathode lamps, a lower contamination due to the absence of the electrodes, and a longer lifetime [START_REF] Klán | Microwave photochemistry[END_REF]. EDLs operate due to free electrons in the fill that are accelerated by the MW field energy. They collide with the gas atoms and ionize them to release more electrons (the "avalanche" effect). The energetic electrons collide with the heavy-atom particles present in the plasma, thus exciting them from the ground state to higher energy levels. The excitation energy is then released as the electromagnetic radiation with the spectral characteristics according to the composition of the fill. There are a number of operating parameters [START_REF] Klán | Microwave photochemistry[END_REF], which have been recognized as influencing the electrodeless discharge lamp performance, such as temperature, nature and pressure of the fill gas, choice of the fill material, dimensions of the lamp envelope, the nature and characteristics of the MW energy coupling device, and the frequency and intensity of the MW energy.

The effect of temperature on EDL is closely associated with the fill gas pressure. At room temperature (T ≈ 300 K) the gas mixture in the lamp has a pressure of approximately 2.6 kPa (0.026 atm), while in an operation mode the temperature of the plasma is most likely between 700 and 1400 K, and the pressure about 1 MPa (10 atm) [START_REF] Spietz | [END_REF]. The plasma includes strong non-equilibrium states due to high-energy particles. The plasma pressure influences the characteristics of the radiation; it affects the mean free path of the particles and their collisional cross-sections. Pressure thus affects the number of collisions per unit of time. Lower-state atoms, which are outside the plasma but still within the lamp volume, have a lower temperature than the emitting atoms within the plasma. Therefore, their absorption line profile is narrower than the emission line profile from the plasma [START_REF] Spietz | [END_REF]. The spatial distribution and relative concentrations of emitting and absorbing atoms critically depend on the partial pressure of the element within the discharge volume: the higher the partial pressure of the element, the more lower-state atoms exist outside the emitting plasma. Therefore, more self-absorbed and -reversed atomic lines result from the simultaneous presence of emitting atoms and lower-state atoms.

The effect of temperature on radiance from EDLs was already investigated [START_REF] Spietz | [END_REF][25][26]. It was found that the optimum operating temperature for the mercury fill is 42 • C (for 253.7 nm line, 6 1 S 0 -6 3 P 1 ). The output is reduced when the temperature is beyond optimum. Operation at high power or high temperatures can increase the intensity but, at the same time, reduce the lifetime and also lead to a broadening of the atomic line profile due to self-absorption and -reversal effects. The temperature dependence of emission intensities from mercury atoms at steady state for lines 365, 405, 436, 546 and 579 nm was also investigated [27]. The increase of the line emission intensities above 37 • C is explained by a decrease of the activation energy of mercury atoms or ions and by reduction of the number of bonds of a mercury atom in plasma. The influence of the lamp cooling by air stream, that can cause a lamp emission instability, was also examined [28]. If the vapor pressure in EDL is too high, the discharge may be limited or even extinguished completely [29].

Effects of temperature

In this work, the temperature-dependent emission spectra of Hg-EDL were determined in various UV-transparent hydrocarbons with boiling points of 35-174

• C (n-pentane, 35 • C; n-hexane, 69 • C; n-heptane, 98 • C; n-decane, 174 • C),
which guaranteed a constant cooling down of the lamp temperature. The lamp temperature was, however, expected to be somewhat higher than that of the solvent. The relative emission peak intensity changes in n-pentane and n-decane are shown in Fig. 2. It is demonstrated that the relative intensity of the 254 nm band is highly dominant at a lower temperature in n-pentane while this band is negligible in n-decane, at the same time the 313 nm band becomes a principal wavelength in the UV region, which is well consistent with the above-described facts. The change of the solvent in a photochemical experiment would therefore dramatically influence the course of the reaction. Figs. 3 and4 show a series of relative peak intensities (normalized on the 254 nm peak) at temperatures from 35 to 174 • C for two differently sized quartz EDLs. The spectra of both lamps present similar dependencies. Generally, the short wavelengths (especially the 254 nm peak) are suppressed with increasing temperature.

Effects of the MW output power

The effect of the MW reactor output power was already investigated in our preliminary paper describing its influence on the Norrish type II reaction efficiency [12]. It was found that the reaction conversion increased with increasing MW power, which boosted the UV irradiance. Fig. 5 shows the emission spectra of a quartz Hg-EDL at two different output power values (30 and 300 W). Relative peak intensities were the same and their dependencies on the power are shown in Fig. 6. All values increased linearly at small power values and then leveled off. This means that the emission efficiency of the lamp has been saturated at ∼200 W power and EDL was not able to accept more MW energy (that was then dissipated to the environment). For routine experiments with EDLs, such an optimization of the power/emission efficiency ratio is highly recommended. The output power of a commercially available MW oven can be lowered by a cooling water in the tube inside the cavity [13], however, it had only minor effect on the lamp emission efficiency in our experiments.

A solvent, which absorbs MW radiation, reduced the intensities of all emission bands since it reduces the amount of MW energy that powers the lamp. The EDL spectrum in methanol (bp = 65 • C) is compared to that in hexane (bp = 69 • C) in Fig. 7. The actual temperature of methanol could be little higher due to the superheating effect [17].

Effects of the EDL envelope material and solvents

Comparative conventional irradiation experiments with either quartz or Pyrex Hg-EDLs were already presented in our previous work [12]. The conversion of valerophenone in the Norrish type II reaction was lowered when a Pyrex Hg-EDL was used, which was explained by filtering off the part of the UV radiation. Fig. 8 shows a comparison of the emission spectra of quartz and as Pyrex Hg-EDLs. The Pyrex glass completely removed the 254 and 297 nm bands, however, the intensity of the remaining bands was the same. This well explains the efficiency loss observed in photochemical experiments. The solvent can be also used as an internal UV filter. Benzene, for example, significantly suppressed the wavelengths below 280 nm (Fig. 9). Therefore, n-hexane or other non-polar solvents, that are transparent over 220 nm, are ideal in case a short-wavelength irradiation is required.

In conclusion, the aim of this paper was to show that the spectrum of the electrodeless discharge lamps can be easily modified by choosing a proper temperature, the EDL envelope glass material, or a solvent used in a photochemical reaction. In addition, the EDL emission intensity could be adjusted by changing the MW output power. Since the application of EDLs in photochemistry is very simple and accessible to a broad spectrum of chemists, it could find its way into a conventional chemical laboratory. Studies of EDLs that contain other filling materials than mercury are currently under investigation.

Fig. 1 .

 1 Fig. 1. Photochemistry in the MW field (adapted according to Klán et al. [13]).

Fig. 2 .

 2 Fig. 2. Emission spectra of EDL in n-pentane and n-decane.

Fig. 3 .

 3 Fig. 3. Temperature dependence of the normalized emission bands in a 9 mm × 13 mm EDL.

Fig. 4 .

 4 Fig. 4. Temperature dependence of the normalized emission bands in a 14 mm × 37 mm EDL.

Fig. 5 .

 5 Fig. 5. Emission spectra of a quartz EDL at the 30 and 300 W output power (Synthewave 402).

Fig. 6 .

 6 Fig. 6. Output power dependence of the emission bands (a quartz EDL; Synthewave 402).

Fig. 7 .

 7 Fig. 7. Comparison of the emission spectra of a quartz EDL in n-hexane and methanol.

Fig. 8 .

 8 Fig. 8. A comparison of the emission spectra of quartz and Pyrex EDLs in n-hexane.

Fig. 9 .

 9 Fig. 9. A comparison of the emission spectra of a quartz EDL in n-hexane and benzene.

Acknowledgements

This work was supported by the Grant Agency of the Czech Republic (203/02/0879). We thank P. Janderka for an opportunity to measure the spectra on an AVS-S2000 spectrometer. We would also like to thank Z. Bohacek and the Czech Geological Survey for the purification and supply of hydrocarbons.