Kais Mekki
email: kais.mekki8@etu.univ-lorraine.fr

William Derigent
email: william.derigent@univ-lorraine.fr

Eric Rondeau
email: eric.rondeau@univ-lorraine.fr

Ahmed Zouinkhi
email: ahmed.zouinkhi@enig.rnu.tnnaceur.abdelkrim@enig.rnu.tn

Mohamed Naceur Abdelkrim

"Toward circular Economy" AN UNIFORM DATA REPLICATION ALGORITHM IN WIRELESS MICRO-SENSOR NETWORK FOR COMMUNICATING MATERIALS APPLICATION

Keywords: Communicating materials, wireless sensor networks, data storage, dissemination protocols

Wireless sensor networks (WSN) have recently gained a great deal of attention as a topic of research, with a wide range of applications being explored such as communicating materials. Data dissemination and storage are very important issues for sensor networks. The problem of designing data dissemination protocols for communicating material needs different analyses related to storage density and uniformity which has not been addressed sufficiently in the literature. This paper details storage protocol on the material by systematic dissemination through integrated wireless micro-sensors nodes. The performances of our solutions are evaluated through simulation using Castalia/OMNeT++. The results show that our algorithm provides uniform data storage in communicating material for different density level.

INTRODUCTION

The communicating material is a new paradigm of industrial information system presented and discussed for the first time in [START_REF] Kubler | Problem definition methodology for the Communicating Material paradigm[END_REF]. It enhances a classic material with the following capabilities: it can store data, communicate information at any point of its surface, and keep these previous properties after physical modifications. This concept leads to an important change in the internet of things. Indeed, the material does not communicate using some tags or nodes in specific points, but becomes intrinsically and continuously communicating. To meet this vision, many ultra-small electronic devices (thousands) are inserted into the material of the product during its industrial manufacturing. The first works focusing on communication materials are presented in [START_REF] Kubler | Embedding data on "communicating materials" from context-sensitive information analysis[END_REF] [START_REF] Kubler | Method for embedding contextsensitive information on "communicating textiles" via fuzzy AHP[END_REF]. It introduces a communicating material (e-textile) obtained by scattering a huge amount of RFID µtags (1500 tags/m²) in a manufactured textile (i.e. µtags manufactured by the Hitachi company, measuring 0.15mmx0.15mm). The system involves a RFID reader/writer connected to a relational database that contains all the product life cycle information. At each writing operation, the database is explored to select the relevant data items (fragments of the database tables) that must be stored in the material. To do so, each data item is assigned an importance level between 0 and 1 (1=highly critical data item, 0=ordinary data item), computed via a multi-criteria decision-making algorithm. Data items with the highest importance levels are then stored in the µtags when the textile passes under the writer module during the manufacturing phases. Since the RFID are memory-constrained, the data item is splitted (segmented) and stored over several tags using a specific protocol header which is also able to rebuild the initial information. This division process is called segmentation and the resulting parts are segments. With such system, the data storage in the communicating material requires a reader/writer connection with each tag. If a tag is not connected during the dissemination phase, it will be isolated and left empty which limits the use of RFID technology in solid and large materials such as concrete and wood. Therefore, our current works propose to use wireless sensor networks (WSN) in such products by spreading ultra-small and micro (even nano (Mahfuz and Ahmed, 2005)) sensor nodes into the material (e.g. 100 nodes/m²). This paper presents dissemination algorithm to store relevant data items in wireless sensor nodes scattered into large scale products such as concrete in smart building (figure 1), plane wings or wood panels. During the industrial construction of a final product and its future use, the communicating material has different life cycle steps as shown in figure 2. In steps 1 and 2, the material undergoes shape transformation (e.g. cutting, sawing, and drilling) to construct the product. Moreover, in the middle life cycle (steps 3 and 4), the material could have possible physical transformation (e.g. broken, piece addition/elimination…) which lead to information loss if data is not replicated in the whole communicating material. Therefore, the information should be stored into the material in uniform way (i.e. data is present in each piece of the material). Indeed, in figure 3(a), the information is not uniformly disseminated, so pieces of the material are empty and information cannot be read after cutting as example. However, figure 3 As consequence, the dissemination algorithm must ensure that data is readable everywhere on the material even. A given data item should be uniformly replicated in the whole WSN of the material.

WSN in communicating material has some specificity:

 High node density (e.g. 100 nodes/m²),  Energy-constrained nodes (Micro-nodes) with limited computation abilities,  Nodes and batteries replacement is impossible (nodes embedded into the material).

Therefore, the dissemination algorithm in such networks and environment has to be judicious. The energy consumption has to be optimized by reduction of the amount of transmitted data in each node as much as possible (i.e. wireless transmission consumes more energy than any other communication activity (Ratnasamy et al., 2002)). Hence, a long communicating material lifetime could be ensured during its life-cycle. The paper is organized as follow. Section 2 is dedicated to related works. Section 3 details the design of our algorithm for data dissemination and storage in communicating materials. Section 4 is dedicated to the presentation of simulation results and performance evaluation. Finally, section 5 concludes the paper.

RELATED WORK

In WSN, data dissemination protocols are used for network reprogramming, routing path discovery (e.g. DSR and AODV), fault tolerance, data mules, and mobile sink management [START_REF] Akdere | A comparison of epidemic algorithms in wireless sensor networks[END_REF]. In past years, various algorithms have been proposed [START_REF] Hongping | Overview of data dissemination strategy in wireless sensor networks[END_REF]. There are two main approaches: reactive and proactive which is divided into structured and unstructured distribution strategies as shown in table 1. In the first one, nodes react to an event (e.g. the presence of mules and mobile sink) by disseminating data towards the nodes that are located close to the event positions, as example Splash, CodeDrip, Typhoon, Supple, and Dynamic Random Replication protocols. In the proactive approach, however, nodes distribute their data towards all or subset of nodes that have the role of storage unit, in anticipation of future event, node failure, mule collection, and others. Under structured dissemination, these storage nodes typically form a virtual structure (e.g. grid, line, and rail) within the WSN that make the data available to be retrieved later (e.g. by a mobile sink visiting the nodes to collect data), as example LBDD, QDD, QAR, DDB, Locators, and ToW protocols. However in unstruc- 2008), DDB [START_REF] Lu | On the data dissemination in wsns[END_REF], QAR (Rumin et al., 2010), ToW [START_REF] Joung | Tug-of-War: An Adaptive and Cost-Optimal Data Storage and Query Mechanism in Wireless Sensor Networks[END_REF]…

DEEP (Vecchio et al., 2010), RaWMS [START_REF] Bar-Yossef | RaWMS: RandomWalk based Lightweight Membership Service for Wireless Ad Hoc Networks[END_REF], MDRW (Viana et al., 2009), RAPID [START_REF] Drabkin | RAPID: reliable probabilistic dissemination in wireless ad hoc networks[END_REF], MOAP (Mukhtar et al., 2009), McTorrent (Huang et al., 2010), SSA [START_REF] Chung | An efficient mechanism for processing similarity search queries in sensor networks[END_REF]), SDS (Shen et al., 2011)…

Reactive

Dynamic Random Replication [START_REF] Cuevas | Dynamic Random Replication for Data Centric Storage[END_REF] , Supple (Viana et al., 2010), CodeDrip (Ribeiro et al., 2014), Typhoon [START_REF] Liang | Typhoon: A Reliable Data Dissemination Protocol for Wireless Sensor Networks[END_REF], Splash [START_REF] Doddavenkatappa | Splash: Fast Data Dissemination with Constructive Interference in Wireless Sensor Networks[END_REF]…

Table 1: Taxonomy from a communication perspective

The aim of this work is to fully disseminate and to uniformly replicate data in the whole communicating material, in order to ease the reading process at any point of the material and to anticipate possible physical transformations. As a result, this section focuses on the protocols related to unstructured proactive dissemination.

An unstructured proactive dissemination protocols in WSN include a broadcast mechanism coupled with a storage strategy both are detailed in the following.

Broadcast mechanisms

Broadcast algorithms are usually referred to as flooding.

Flooding is an important algorithm in WSN and is applied when a source node has to send information to subset or all nodes in the network. This is achieved by broadcasting a packet to the entire neighborhood. Each node that receives the packet rebroadcasts it, if it has not been forwarded previously. In this way, the information traverses the whole network and reaches all the nodes which then decide to store it or not according to the storage strategy. Although flooding is a very simple and efficient for data dissemination, it has some deficiencies. The main problems are: duplication (i.e. a node is forced to get information twice from two different nodes), collision (i.e. the broadcast increases the contention), and resource blindness (i.e. nodes do not adopt energy saving mechanisms) [START_REF] Busnel | Gossiping over storage systems is practical[END_REF]. As a consequence, various schemes for controlled flooding have been proposed including probabilistic-based also referred to as gossiping (Sekkas et al., 2010) [START_REF] Kyasanur | Smart gossip: an adaptive gossip-based broadcasting service for sensor networks[END_REF]), counter-based (Miranda et al., 2006) (Zhi-yan et al., 2007), distance-based and location-based (Sanchez et al., 2011).

Distance-based and location-based schemes exploit distance between nodes and their position. Therefore, nodes need to be equipped with a Received Signal Strength Indicator (RSSI) or a Global Positioning System (GPS). Such additional functions could not be applied in communicating materials since the nodes are embedded in the product with a high density (i.e. nodes are very close).

Authors in [START_REF] Garbinato | Injecting power-awareness into epidemic information dissemination in sensor networks[END_REF] [START_REF] Izumi | Improvement of Counter-based Broadcasting by Random Assessment Delay Extension for Wireless Sensor Networks[END_REF] show that the counter-based scheme outperforms the probabilistic one in terms of reliability (i.e. the percentage of nodes that are reached on average) and efficiency (i.e. the average amount of resources required for broadcasting a message). This mechanism can reduce the number of retransmitting nodes with a high arrival rate. Furthermore, it doesn't require specific hardware such as distance and location based schemes.

Storage strategies

Storage strategies in unstructured proactive dissemination protocols are developed in the literature according to the target application. In (Maatta et al., 2010) [START_REF] Dong | Bulk data dissemination in wireless sensor networks: Modeling and analysis[END_REF], the information is replicated in each node for network reprogramming. If a node does not receive the new version of binary code, its software is not updated and is isolated. Authors in (Maia et al., 2013) (Neumann et al., 2010) [START_REF] Gonizzi | Data dissemination scheme for distributed storage for IoT observation systems at large scale[END_REF] propose storage strategies to improve network resilience against the risk of nodes failures. The storage nodes are selected according to some critical parameters such as connectivity, available memory and remaining energy. DEEP (Vecchio et al., 2010) adopts another storing strategy for an effective data collection by mobile sink with uncontrolled trajectory. Every node that receives a data stores it with a probability P according to the number of information that must be replicated in each node. In [START_REF] Bar-Yossef | RaWMS: RandomWalk based Lightweight Membership Service for Wireless Ad Hoc Networks[END_REF], authors use a hopcounter mechanism to replicate data for mobile sink collection. Hop-counter is a process in which a data is repeatedly forwarded from a node to one of its chosen neighbors. At the same time, the number of hops realized from the source node is count. When the counter is equal to a predefined value, the information is stored in the current node.

Synthesis

The above storing strategies use the physical nodes parameters (e.g. memory and energy), network topology, and neighborhood awareness to replicate data throughout the WSN. They don't assume the properties and the characteristic of the disseminated data itself. This problem is the key contribution of our work, in which a dynamic information-based dissemination protocol is proposed and applied on the communicating material framework. In this current paper, the data importance level is processed. The item with a high level must be replicated more than the lower one. The dissemination algorithm has to ensure the uniformity of replication for each level. For these reasons, probabilistic approach coupled with counter-based flooding is developed in this article and the results are evaluated from a uniformity and energy consumption point-of-view. The developed solution represents an integration of a probabilistic storage approach in an energy-efficient counter-based broadcasting scheme. The replication process is controlled within each neighborhood using a packet header named Node-Storage (NS). All data seg-MOSIM'14 -November 5-7-2014 -Nancy -France ments, hence the information, are forced to be stored only once in every neighborhood which ensure the same storage density and highly uniformity throughout the communicating material. The algorithm is simulated with Castalia/OMNeT++ using a realistic collision model. It is evaluated by studying the uniformity of replication for different importance levels and the energy consumption rate. The rest of the paper details the proposed data dissemination and replication algorithm, and then presents simulation results obtained for different data item importance level.

DATA REPLICATION ALGORITHM

This section presents our algorithm of data item dissemination. Firstly, the concept of master node is defined, then the counter-based forwarding scheme is described, and finally the dissemination/replication algorithm is presented.

Master Node

When the user has to disseminate information, he connects to the communicating material through a selected node in its transmission range which is named in this paper "master node" (i.e. any node in the material could be a master node). The Master node is responsible for forwarding user requests, collecting response messages from all nodes, and disseminating data items. Some constraints could be used to select the master node such as the highest residual energy level.

Counter-based scheme

In simple flooding, the more duplicate broadcasted packets a node receives, the less effective its rebroadcasting becomes [START_REF] Lu | On the data dissemination in wsns[END_REF]. This is because the duplicate messages are likely to have been received by its neighboring nodes. This fact is involved in the counter-based flooding. In this scheme, a node that has received redundant packet more than a predefined threshold Cth cancels the rebroadcasting. The counterbased algorithm is shown below:

1. When a node receives a broadcasted packet for the first time, the node initializes a counter N to one, and sets a random assessment delay (RAD) at a value between 0 and T max .

2. If the node receives the same broadcasted packet during the RAD, it increments the counter N (NN+1). If the counter reaches a preset threshold Cth, it cancels the rebroadcasting.

3. After the RAD expires, the node rebroadcasts the packet to all neighbors.

Figure 5 summarizes the algorithm of this scheme in each node. This scheme is improved in (Mekki et al., 2014) for a long communicating material lifetime. The random waiting delay is processed to prevent as much as possible the nodes with low energy level from rebroadcasting. As consequence, the rate of rebroadcasting of each node is adapted to their remaining energy. In the original counter-based scheme, node selects random waiting delay RAD between 0 and a fixed period of time T max . To adopt this mechanism to the remaining energy level of node, the equation 1 and 2 are developed.

Algorithm: Counter-Based Broadcasting Scheme

RAD extension = RAD x K K = (E initial / E remainder )
RAD extension : random assessment delay extension. E initial : initial energy of sensor node. E remainder : remainder energy of sensor node.

RAD is multiplied by K which increases the waiting delay when the remainder energy becomes lower as shown in figures 6 and 7. If the remainder energy is very low, the waiting delay RAD extension is larger. So, the node has to wait more than others and count the number of retransmission N of neighbor nodes which have more energy. As consequence, it has more chance to reach the counter threshold Cth and cancel the rebroadcast. Simulation results in (Mekki et al., 2014) showed that this delay extension maintain the high reachability of the original scheme. And, it enhances the communicating material lifetime by reducing the number of retransmitting nodes by about 10% (i.e. optimizes more the energy consumption than the original counter-based scheme). Moreover, the optimal parameters of the improved scheme was fixed to T max =200ms and Cth=4 for high reachability and number of retransmitting node trade-off. Such parameters are consistent with previous optimized interval values for the counter-based scheme as described in [START_REF] Arango | Retransmission and Back-off Strategies for Broadcasting in Multi-hop Wireless MOSIM'14 -November 5-7-2014 -Nancy -France Networks[END_REF] and [START_REF] Jacobsson | An experimental investigation of optimized flooding protocols using a wireless sensor network testbed[END_REF].

Storage strategy

The master node starts the dissemination process. The packet is broadcasted from one node to all its neighbors using the improved energy-efficient counter-based flooding scheme. The storage strategy is integrated in this broadcasting scheme. The node stores the data with a probability P which is equal to the importance level as shown in figure 9 and equation 3 (i.e. the master the node start dissemination process by associating the probability P=I to the data).

F : [0,1]→[0,1] I → P=I
With I is the importance level of the data item. To ensure a uniform and controlled replication rate of data segments, a packet header named Node-Storage (NS) is defined which takes two values 0 and 1. When a node stores a data, it rebroadcast packet with NS=1 to inform neighbor nodes, hence replicate data once within each neighborhood. Else, NS is set to 0.

The extended scheme works as follows:

1. When a node receives a broadcasted packet for the first time, the node initializes a counter N to one, and sets a RAD at a value between 0 and T max . If the received packet has NS=1, the storage decision is cancelled and NS is set to 0.

During the RAD:

-If the node receives the same broadcasted packet with NS=1, the storage decision is cancelled and NS is set to 0. -If the counter reaches the preset threshold Cth, it cancels the rebroadcasting.

3. After the RAD expires: This process continues until the edges of material are reached. Our dissemination algorithm is broadcast based. With such communication model, the packet is further processed by nodes that have already received it. Furthermore, user can disseminate many segments or other data items. So, each packet is identified in the network layer (Packet ID) and then the nodes are limited to accept the same message only once. If a node receives again a treated message with the same ID, it will be dropped. Figure 8 shows the flowchart of this process executed in each node.

SIMULATION AND PERFORMANCE EVALUATION

This section describes the details of the simulation setup, the used parameters and finally simulation results of data replication.

Simulation setup

The performance of our solution is evaluated through simulation. Castalia/OMNET++ simulator is used to implement our dissemination algorithms. Currently, many wireless sensor network simulators are available as COOJA and TOSSIM but Castalia provides realistic wireless channel, radio models, and node behavior (Sundani et al., 2011). We simulate the Tyndall 10mm sensor node (Shen et al., 2009) which is one of the smallest nodes used in the industry and research (10mm x 10mm). Thus, a thousand of scattered nodes in small material size could be simulated. The simulated communicating material consists of 2500 nodes. The nodes positions are all uniformly distributed within a 50mx50mx1m square (1 node/m 3 ) and the transmission power -25 dbm is used which ensures that a given node can communicate with 25 neighbors in average. The chosen simulation parameters are summarized in table 3.

Simulation results

The impacts of the storage strategies on the uniformity performance for the proposed data dissemination algorithms have been investigated, through Castalia simulation. Figure 11 and 12 show the simulation results of the broadcast and storage mechanisms of segmented data item for different importance level (i.e. different storage probability).

On the basis of the obtained results, the following observation can be carried out. The data is well replicated in the communicating material according to the importance level (probability of storage). When a low importance is used, as example P=0.1, data item is stored in a maximum of 380 nodes in the communicating material. Furthermore, the number of storage nodes does not highly increased for large probability value, Moreover, segments are replicated with the same density (no segment is stored much than others) as a controlled neighborhood storage strategy is used (i.e. the segment is stored once in each neighborhood as much as possible).

MOSIM'14 -November 5-7-2014 -Nancy -France

Uniformity and energy performances

The uniformity and energy consumption performance of our algorithm is compared to DEEP which is a recent protocol of data dissemination and replication in WSN. DEEP strategy is based on efficient probabilistic forwarding and storing strategy. The probabilistic forwarding is inherited from the RAPID dissemination mechanism [START_REF] Drabkin | RAPID: reliable probabilistic dissemination in wireless ad hoc networks[END_REF]. In particular, the data dissemination strategy of this approach employs a combination of density sensitive probabilistic forwarding with deterministic corrective measures, as described in [START_REF] Drabkin | RAPID: reliable probabilistic dissemination in wireless ad hoc networks[END_REF]. Essentially, the goal of RAPID is to ensure that there will be a predefined average number of retransmissions of each message in each neighborhood. This is obtained as follows: each node p that receives a message m for the first time, decides to rebroadcast m immediately with a probability F presented in equation 4:

Where N(p) is the one-hop neighborhood of p (i.e. the number of nodes within the radio transmission range of p) and β is the desired average number of retransmissions in each neighborhood. Additionally, to overcome situations in which, due to the probabilistic nature of this process, no node decides to transmit in a given neighborhood, RAPID complements this with a semideterministic corrective measure. Specifically, if a given node q decides not to retransmit m but does not hear any other retransmission of m after a (relatively long) randomly selected period of time, then q rebroadcast m after all. So, DEEP take the above described broadcasting scheme of RAPID and couple it with a storing strategy by which every node that receives a message carrying the disseminated data, stores it with probability P. DEEP is implemented in Castalia, and is simulated for each storage probability P to replicate segments in the whole communicating material.

Uniformity performance

To study the uniformity performance, a neighborhood window is used as shown in figure 13.

Figure 13: Uniformity evaluation method

The NxN window represents the one-hop neighborhood of a node i. The window is moved around all nodes in the material, and each time, we check the existence of segments within this window (i.e. the information exists in each neighborhood if the three segments are present). The 5x5 window is the window that analyzes the performance of our simulation scenario as the simulated average neighborhood density is equal to 25 nodes. As shown in figure 14 and 16, all segments are presents and hence the information exists for 100% of nodes in the material for a probability higher than 0.4, and, above 93% for storage probability between 0.2 and 0.3. However, the uniformity decrease for 0.1 as the used storage probability is very low (i.e. the information exists for 60% of nodes).

For DEEP algorithm, if the storage probability is increased above 0.4, the uniformity decrease, as the segments are not replicated with the same density as shown in figure 15. The first disseminated segment is replicated more than others, for example the segment 3 is not stored in any nodes for probability P=1 as all nodes are occupied by first segments, hence the information could not be read in any part of the material (information existence ratio=0%). This shows the advantage to use NS packet header in our algorithm because it allows controlling the segment storage rate in the material. All segments are replicated for any storage probability within each neighborhood. So, the proposed algorithm is considered as highly uniform and the information could be read in each pieces of the material even after a shape transformation. Moreover, for potential future reading (step 3 of the material life cycle (figure 2)), the master node won't need to send request for more than 1 hop or 2 hops in the material (i.e. the multi-hop connectivity is not required for stored data gathering). The information could be read using a simple neighborhood request/response, which optimize and reduce the energy consumption as no long communication is needed (i.e. energy efficient information retrieval).

Energy consumption performance

In this study, the average consumed energy by all nodes is measured after the end of the dissemination/replication process. The consumed energies of the proposal algorithm and DEEP are shown in figure 17.

Figure 17: Energy consumption performance of the proposed algorithm versus DEEP

Figure 17 shows that our algorithm consume less energy than DEEP after the dissemination process, as an energyefficient counter-based scheme is used to broadcast packets in the whole material. The probabilistic-based forwarding scheme of DEEP is not a deterministic scheme which could lead to many collision and interference, hence retransmissions and more energy consumption. The random delay extension adopted for our algorithm keeps a lot of energy and gives an equitable use of nodes resources (energy balance). Therefore, our solution optimizes more the energy consumption and ensures a long communicating material lifetime (i.e. network lifetime) for a dissemination and replication process.

CONCLUSION

In this paper, a data dissemination algorithm is proposed to replicate information in communicating materials through scattered ultra-small wireless sensor nodes. A counter-based flooding approach and probabilistic strategy are used to distribute the packets to the whole network and to control the replication rate according to the importance level of the user information. The performances of the solution are evaluated through computer simulation. Simulation results show that our algorithm provides uniform replication for different storage probability. However, if a very low importance value is used (0.1) on the dissemination phase, large empty areas and low uniformity are obtained which can lead to data loss during physical material transformation.

Our future works will be focused on testing the proposed algorithm in real WSN using IoT-LAB platform (www.iot-lab.info).

Figure 1 :

 1 Figure 1: Application of communicating material concept in smart building

Figure 3 :

 3 Figure 3: Uniform replication for anticipation of material transformation

Figure 4 :

 4 Figure 4: Storage density according to the importance level

Figure 2 :

 2 Figure 2: Communicating material life cycle

Figure 5 :

 5 Figure 5: Algorithm of the counter-based broadcasting scheme

Figure 6 :

 6 Figure 6: Remainder energy versus the fraction E initial /E remainder for E initial =100J

Figure 9 :

 9 Figure 9: Conversion function of data importance to storage probability

Figure 8 :

 8 Figure 8: Data replication algorithm in communicating material

Figure 10 :

 10 Figure 10: Tyndall node

Figure 12 :

 12 Figure 11: Simulation results

  Figures 14, 15, and 16 show the uniformity performance for windows 5x5 (25 nodes), of our proposed algorithm and DEEP protocol.

Figure 14 :

 14 Figure 14: Existence ratio of segments in each neighborhood for the proposed algorithm

  -The node stores the data with a probability P, computed with the conversion function F from the importance level (see table 2), put NS to 1 and rebroadcast the packet even if the counter Cth of broadcasting scheme is reached. As an example, let P=0.5.

	The
	node selects a random value rand within
	the interval [0,1]. The node stores the data
	item only if rand≤P(=0.5).
	-If the storage decision is cancelled and the
	counter Cth is not reached, the node re-
	broadcast the packet to all neighbors.

  Table 2 and figure 10 show the characteristic of Tyndall 10mm node.

	Size		10 mm by 10 mm
	Microcontroller	Atmega128	Single chip
	Transceiver	Nordic	transceiver/microcontroller
		nRF905
	Frequency bands	433 MHz, 868 MHz, and 915 MHz
	Bandwidth		50 kbps
	Memory		4 kb
	Microcontroller		688 µA
	Unit
	Transmission mode		10.35 mA
	Reception mode		13.32 mA
	Moderate sleep		166 µA
	Deep sleep		3.3 µA

Table 2 :

 2 Tyndall 10mm node characteristics

Table 3 :

 3 Simulation parameters

	Material size	50mx50mx1m
	Number of nodes	2500
	Nodes distribution	Grid 50x50
	Nodes density	1 node/m 3
	Average neighborhood	25 nodes
	density
	Location of master node	Center of the material
	Mac Protocol	TMAC
	Sigma channel parameter	4
	BidirectionalSigma chan-	1	Real radio
	nel parameter		wireless
	Radio Collision Model	Additive inter-	channel in
		ference model	Castalia
	Cth	4
	Tmax	200 ms
	Number of data segments	3
	Simulation time	100s
	Number of trials	60

MOSIM' 14 -November 5-7-2014


Kais Mekki

William Derigent

Ahmed Zouinkhi

Eric Rondeau
email: eric.rondeau@univ-lorraine.frahmed.zouinkhi

Mohamed Naceur Abdelkrim
email: naceur.abdelkrim@enig.rnu.tn

Data dissemination algorithms for communicating materials using wireless sensor networks

Keywords: communicating materials, wireless sen sor networks, data dissemination I

come

INTRODUCTION

The communicating material is a new paradig m of industrial information system presented and discussed for the first time in [START_REF] Kubler | Problem definition methodology for the Communicating Material paradigm[END_REF]. It enhances a classic material with the following capabilities: it can store data, communicate information at any point of its surface, and keep these previous properties after physical modifications. This concept leads to an important change in the internet of things. Indeed, the material does not communicate using some tags or nodes in specific points, but becomes intrinsically and continuously communicating. To meet this vision, many ultra-s mall electronic devices (thousands) are inserted into the material of the product during its industrial manufacturing.

The first works focusing on communication materials are presented in [START_REF] Kubler | Method for embedding context -sensitive information on "communicating textiles" via fuzzy AHP[END_REF][START_REF] Kubler | Embedding data on "communicating materials" from context -sensitive information analysis[END_REF]. It introduces a communicating material (etextile) obtained by scattering a huge amount of RFID µtags (1500 tags/m²) in a manufactured textile (i.e. µtags are tags manufactured by the Hitachi co mpany, measuring 0.15mmx 0.15mm). The system involves a RFID reader/writer connected to a relational database that contains all the product life cycle information. At each writing operation, the database is explored to select the relevant data items (frag ments of the database tables) that must be stored in the material. To do so, each data item is assigned an importance level between 0 and 1 (1=h ighly critical data item, 0=ordinary data item), computed via a mu lti-criteria decision-making algorith m. Data items with the highest importance levels are then stored in the µtags when the textile passes under the writer module during the manufacturing phases. Since the RFID are memo ryconstrained, the data item is splitted (segmented) and stored over several tags using a specific protocol header which is also able to rebuild the initial information. Th is division process is called segmentation and the resulting parts are segments 1 .

With such system, the data storage in the communicating material requires a reader/writer connection with each tag. If a tag is not connected during the dissemination phase, it will be isolated and left empty which limits the use of RFID technology in solid and large materials such as concrete and wood. Therefore, our current works propose to use wireless sensor networks (WSN) in such products by dispersing ultrasmall and micro (even nano [START_REF] Mahfuz | A review of micro-nano-scale wireless sensor networks for environmental protection: Prospects and challenges[END_REF]) sensor nodes into the material (e.g. 100 nodes/m²).

This paper presents dissemination algorithms to store relevant data items in wireless sensor nodes scattered into large scale products. Different storage modes are envisaged as shown in figure 1: non-localized storage (i.e. informat ion is spread all over in the whole material) and localized storage (i.e. information is located in a limited region of the material). In the non-localized mode, the algorith m should respect the following constraint: data must be readable everywhere on the material even after a shape transformation (e.g. cutting, sawing, and drilling). So, a given data item should be replicated in a uniform way. Indeed, in figure 2(a), the information is not uniformly disseminated, so pieces of the material are empty and information cannot be read after cutting as examp le. However, figure 2(b) shows a uniform replication where the information could be read in each piece. Furthermore, to disseminate information, user sends data item coupled with an importance level. So, the number of nodes storing this data should depend on this level as shown in figures 3(a) and 3(b). Therefore, the dissemination algorith m has to keep the uniformity of data item replication in the material whatever the importance level. The proposed algorithms are simulated with Castalia/OMNeT++ using a realistic collision model. They are evaluated by studying the uniformity of replication for segmented and non-segmented data items of different importance levels.

The paper is organized as follow. Section 2 is dedicated to related works. Section 3 details the design of our algorithms for data dissemination in the communicating materials. Section 4 is dedicated to the presentation of simu lation results and performance evaluation. Finally, section 5 concludes the paper.

II. RELATED WORK

In WSN, data dissemination protocols are used for network reprogramming, routing path discovery (e.g. DSR and AODV), fault tolerance, data mules, and mobile sink management [START_REF] Akdere | A comparison of epidemic algorithms in wireless sensor networks[END_REF]. In the past years, various algorithms have been proposed [START_REF] Hongping | Overview of data dissemination strategy in wireless sensor networks[END_REF]. There are two main approaches: reactive and proactive which is divided into structured and unstructured distribution strategies as shown in figure 4. In the first one, nodes react to an event (e.g. the presence of mules and mobile sink) by disseminating data towards the nodes that are located close to the event positions. In the proactive approach, however, nodes distribute their data towards all or subset of nodes that have the role of storage unit, in anticipation of future event, nodes failure, mu le co llection, and others. Under structured dissemination, these storage nodes typically form a v irtual structure (e.g. grid, line, and rail) within the WSN that make the data available to be retrieved later (e.g. by a mobile sink visiting the nodes to collect data). However in unstructured dissemination, the data is replicated throughout the whole network. The aim of this work is to fully disseminate and to uniformly replicate data in the whole communicating material, in order to ease the reading process at any point of the material and to anticipate possible physical transformations. As a result, this section focuses on the protocols related to unstructured proactive dissemination.

Unstructured proactive dissemination protocols in WSN include a broadcast mechanism and a storage strategy, both detailed in the following.

A. Broadcast mechanisms

Broadcast algorithms are usually referred to as flooding. Flooding is an important algorithm in WSN and is applied when a source node has to send informat ion to subset or all nodes in the network. Th is is achieved by broadcasting a packet to the entire neighborhood. Each node that receives the packet rebroadcasts it, if it has not been forwarded previously. In this way, the information traverses the whole network and reaches all the nodes which then decide to store it or not according to the storage strategy. Although flooding is a very simple and efficient for data dissemination, it has some deficiencies. The main problems are: duplication (i.e. a node is forced to get information twice fro m two different nodes), collision (i.e. the broadcast increases the contention), and resource blindness (i.e. nodes do not adopt energy saving mechanisms) [START_REF] Busnel | Gossiping over storage systems is practical[END_REF]. As a consequence, various schemes for controlled flooding have been proposed including probabilistic-based also referred to as gossiping [START_REF] Sekkas | Probabilistic information dissemination for MANET s: the IPAC approach[END_REF] [9], counter-based [START_REF] Miranda | A power-aware broadcasting algorithm[END_REF] [START_REF] Zhi-Yan | An energy-aware broadcast scheme for directed diffusion in wireless sensor network[END_REF], distance-based and location-based [START_REF] Sanchez | Performance evaluation of reliable and unreliable opportunistic flooding in wireless sensor network[END_REF].

Distance and location based schemes explo it distance between nodes and their position information. Therefore, nodes need to be equipped with a Received Signal Strength Indicator (RSSI) or a Global Positioning System (GPS). Such additional functions could not be applied in commun icating material since the nodes are embedded in the product with a h igh density (i.e. nodes are very close).

Authors in [START_REF] Garbinato | Injecting powerawareness into epidemic information dissemination in sensor networks[END_REF] [START_REF] Izumi | Improvement of Counter-based Broadcasting by Random Assessment Delay Extension for Wireless Sensor Networks[END_REF] show that the counter-based scheme outperforms the probabilistic one in terms of reliab ility (i.e. the percentage of nodes that are reached on average) and efficiency (i.e. the average amount of resources required for broadcasting Data dissemination protocols in WSN Proactive Reactive Structured Unstructured Ring a message). This mechanism can reduce the number of retransmitting nodes with a high arrival rate. Furthermore, it doesn't require specific hardware such as distance and location based schemes. For these reasons, the counter-based strategy is adopted in our dissemination protocol as broadcast mechanism.

B. Storage strategies

Storage strategies in unstructured proactive dissemination protocols are developed in the literature according to the target application. In [START_REF] Kulkarni | MNP: Multihop Network Reprogramming Service for Sensor Networks[END_REF] [16], the informat ion is replicated in each node for network reprogramming. If a node does not receive the new version of binary code, its software is not updated and is isolated. Authors in [START_REF] Maia | A distributed data storage protocol for heterogeneous wireless sensor networks with mobile sinks[END_REF] [18] [19] propose storage strategies to improve network resilience against the risk of nodes failures. The storage nodes are selected according to some critical parameters such as connectivity, available memory and remaining energy. DEEP [START_REF] Vecchio | DEEP: Densitybased proactive data dissemination protocol for wireless sensor networks with uncontrolled sink mobility[END_REF] adopts another storing strategy for an effective data collection by mobile sink with uncontrolled trajectory. Every node that receives a data stores it with a probability P according to the number of information that must be replicated in each node. In [START_REF] Bar-Yossef | RaWMS -RandomWalk based Lightweight Membership Service forWireless Ad Hoc Networks[END_REF], authors use a hopcounter mechanism to replicate data for mobile sink co llection. Hop-counter is a process in which a data is repeatedly forwarded from a node to one of its chosen neighbors. At the same time, the number of hops realized fro m the source node is count. When the counter is equal to a predefined value, the information is stored in the current node.

C. Synthesis and proposed solution

The main broadcast and storage approaches for data dissemination protocols in WSN could be summarized in two categories as shown in table 1. The above storing strategies use the physical nodes parameters (e.g. memory and energy), network topology, and neighborhood awareness to replicate data throughout the WSN. They don't assume the properties and the characteristic of the disseminated data itself. This problem is the key contribution of our work, in which a dynamic information-based dissemination protocol is proposed and applied on the communicating material framework.

In this current paper, the data importance level is processed. The item with a high level must be replicated more than the lower one. The dissemination algorithm has to ensure the uniformity of replication for each level.

In the literature, few works try to compare data dissemination protocols in the whole wireless sensor network, and, to our knowledge, the replication uniformity is not studied. For these reasons, probabilistic and hop-counter approaches coupled with counter-based flooding are developed in this article and the results are compared from a uniformity point-of-view. The rest of the paper details the proposed data dissemination algorithms, and then presents simulation results obtained for different data item importance level.

III. DATA DISSEMINATION ALGORITHMS

This section presents our different strategies and algorithms of data item dissemination. Firstly, the concept of master node is defined, then the counter-based forwarding scheme is described, and finally the dissemination algorithms are presented.

A. Master Node

When the user has to disseminate information, he connects to the communicating material through a selected node in its transmission range which is named in this paper "master node". Master node is responsible for forwarding user requests, collecting response messages from all nodes, and disseminating data items. So me constraints could be used to select the mater node such as the highest residual energy level.

B. Broadcast strategy : counter-based scheme

Our dissemination protocol adopts the counter-based forwarding scheme. When node receives a new packet, it fixes a random waiting delay before making the forwarding decision. During this delay, the sensor counts the number of retransmissions of the same packet by neighbor nodes. After the waiting delay has elapsed, the packet is only forwarded if the number of retransmissions N is smaller than a predetermined threshold. As a result, the forwarding ratio depends on the nodes density: in lo w density areas this ratio will be high, whereas in high density areas it will be low. Different methods could be used for threshold selection such as those described in [START_REF] Tseng | Adaptive Approaches to Relieving Broadcast Storms in a Wireless Multihop Mobile Ad Hoc Network[END_REF] [START_REF] Chen | DISCOUNT: A Hybrid Probability -Based Broadcast Scheme for Wireless Ad Hoc Networks[END_REF]. In [START_REF] Tseng | Adaptive Approaches to Relieving Broadcast Storms in a Wireless Multihop Mobile Ad Hoc Network[END_REF], a node sets the value of the counter threshold according to the number of its neighbor nodes. Similarly, in [START_REF] Chen | DISCOUNT: A Hybrid Probability -Based Broadcast Scheme for Wireless Ad Hoc Networks[END_REF], a node sets it according to the distance from the broadcasting node to itself. Authors in [START_REF] Arango | Retransmission and Back-off Strategies for Broadcasting in Multi-hop Wireless Networks[END_REF] show that a threshold between 4 to 6 retransmissions is preferable fro m the viewpoint of the trade-off between reliability and efficiency. Moreover, simulation results in [14] [25] show that if the threshold is 4 or more, the reliability is over 99.5%. As the communicating material has high density embedded sensor nodes (e.g. 100 nodes/m²), a threshold of 4 is used for the counter-based scheme of our dissemination algorithms.

C. Storage strategies

This section presents two approaches for data item replication in the entire material using the importance level as a parameter. These are respectively hop-counter based and probabilistic-based.

Let us remind that a data item is coupled with an importance level. One part of the storing strategy thus consists in converting this level into a dissemination level computed once by the master node and sent to the sensor network. As a result, for each approach, a conversion function F is defined. In the case of a localized storage, the storage zone must be reduced to an area centered on the master node. As a result, for each approach, a limitation function G is defined, which adapts the dissemination level (previously computed by the master node using F) to the storage zone. This function is executed on each node receiving a data item (see table 2).

1) Hop-counter-based storage

The master node starts broadcasting the packet with a counter value Hop equal to H 0 , computed with the conversion function F 1 from the importance level (see table 2). The packet is broadcasted from one node to all its neighbors, and at each hop the counter is decremented (HopHop-1) until it reaches zero. In this case, the node must store the data, then resets Hop to its initial value (H 0 ), and rebroadcasts the packet within its neighborhood. This process continues until the edges of material are reached. Note that H 0 is also included in the packet.

Our dissemination algorithm is broadcast based. With such communication model, the packet is further processed by nodes that have already received it. Furthermore, user can disseminate many segments or other data items. So, each packet is identified in the network layer (Packet ID) and then the nodes are limited to accept the same mess age only once. If a node receives again a message with the same ID, it will be dropped. Figure 5 shows the flowchart of this process executed in each node.

2) Probabilistic-based storage

In this solution, data packets are flooded throughout the whole material and reach all sensor nodes using the counterbased flooding process. Each node decides to locally store data with the probability P determined by the conversion function. As an examp le, let P=0.5. Upon receiving a data packet, a node selects a random value rand within the interval [0,1]. The node stores the data item only if rand≤P(=0.5). Figure 6 shows the flowchart describing this process executed in each node. Only -NR: number of replications, incremented each time a same data packet is replicated on a node, -maxNR: maximum number of replication of a same data packet.

Computed at start, it is related to H0 and R, the radius of the storage zone given by user:

Retransmission of a same data packet from node 0 to node 5 (see figure 7 for more details)

G2: ℝ→ℝ P → P'=P/K

With:

-K: a limitation constant, given by user. This constant is related to the storage zone. The higher it is, the smaller the storing zone.

Retransmission of a same data packet from node 0 to node 5 (containing the data item + P value)

the storage part is illustrated, since the counter-based flooding is the same.

Fig. 6. Flowchart of probabilistic data dissemination process.

D. Localized storage strategy

Both strategies can be completed with limitation functions defined to restrain the storage zone around the master node. For the hop-counter-based approach, G 1 is defined as a piecewise linear function, that influences the value of H 0 (see table 2). Figure7 describes the effect of the limitation function on H 0 . Indeed, the data packet containing the data item is first sent fro m the master node (n°0) to the network sensor. When the number of replications NR (number of times the data item has been stored) becomes higher than MaxNR, then the value of H 0 is changed to MaxHop. This new value thus prevents any other sensors to store the data item contained in this data packet. For the probability-based approach, the limitation function G 2 is a division of the initial probability by a constant K (see table 2). Node after node, the probability decreases, moving closer to 0, which prevents nodes to store the data item.

IV. SIMULATION AND PERFORMANCE EVALUATION

In this section, the simulation setup is described firstly. Then, the simulation results are presented and discussed.

A. Simulation setup

The performance of our solution is evaluated through simulation. Castalia/OMNET++ simu lator is used to implement our dissemination algorithms. Currently, many wireless sensor network simulators are available as COOJA and TOSSIM but Castalia provides realistic wireless channel, radio models, and node behavior [START_REF] Sundani | Wireless Sensor Network Simulators, A Survey and Comparisons[END_REF].Nevertheless, Castalia is modified to support communicating material applications. A specific radio module is developed which is consistent with CC2420 co mponents but allows more reduced transmission range. Thus, a thousand of scattered nodes in small material size could be simulated.

The simu lated communicating material consists of 2500 nodes. The nodes positions are all uniformly distributed within a 5m x 5m square (100 nodes/m²) and the transmission power -90 dbm is used which ensures that a given node can only communicate with its direct neighbors. The chosen simulation parameters are summarized in table 3.

B. Simulation results of non-localized dissemination 1) Non-segmented data item

The impacts of the storage strategies on the uniformity performance for the proposed data dissemination algorith ms have been investigated, through Castalia simulation. Figures 8 and9 show the simu lation results of the broadcast and storage mechanisms of hop-counter and probabilistic replication of a data item for different importance level (i.e. different hop and different probability, respectively). On the basis of the obtained results, the following observation can be carried out. Both figures show the high reliability of the counter-based broadcasting scheme as all nodes store the information for Hop=0 and for P=1. Furthermore, the data is replicated in the commun icating material according to the importance level (hop counter and probability of storage). When a low importance is used, for examp le P=0.4, data item is stored in 1000 nodes and less in the communicating material. The density of storage decreases with the importance level using the two algorithms. To study the uniformity performance, the simulated material is divided into small cells of 50 cm x 50 cm as shown in figure 10. Each piece has 25 sensor nodes as a uniform distribution (grid 50x50) is used. In each cell, the number of nodes that have stored the data item is counted. Figure 11 shows the storage density difference between cells, using the standard deviation tool. As the standard deviation presents the variable dispersing around its mean, the figure shows that the probabilistic algorith m has a resulting dissemination more uniform than the one obtained with the hop-counter approach.

When low importance is used (0.5 and less), the standard deviation decreases for the hop-counter algorithm (i.e. the uniformity is worst with large counter value). In that case, there are many pieces that are completely empty and others are poorly filled. However, there is no significant decrease of standard deviation for the probabilistic-based strategy because data item is uniformly replicated and many pieces are filled for all the importance level. Therefore, the hop-counter strategy produces more empty areas in the communicating material than the probabilistic-based in case of low importance level which could lead to data loss during material transformation. The three segments (S1, S2 and S3) are disseminated with the same hop-counter value (Hop) for the first algorithm and the same probability P for the second one. Black, green, and blue colors present the nodes that have stored the first, second, and third segments, respectively.

2) Segmented data item

Figure 12 shows that all the disseminated segments have the same density in the material for large hop-counter (Hop=6 and more) as the proposed algorithm tries to make exactly the same replicas of all broadcasted packets. However, like the non-segmented data items, there are more empty areas when the number of hops increases . Furthermore, if large size segments are disseminated with a low number of hops (e.g. Hop=0), the first broadcasted item is stored in all available memories of nodes and prevents the other segments to be replicated. As examp le, for Hop=2, the first segment is stored in more than 837 nodes, the second in 702 nodes and the last segment is replicated in 529 nodes.

This problem is more clear is figure 13 for probabilistic replication strategy. The first segment is replicated in a large number of nodes than the others for high importance level which present a deficiency problem of memory space. As examp le for storage probability P=0.6, the first segment is replicated in 1473 nodes and the last one is stored in 213 nodes.

A solution to these problems could rely on a dynamic selforganization of the data item storage, in which node autonomously decides to replicate or not. The replication process may uses a storage control approach similar to neighborhood negotiation to know if the data has already been stored by other nodes [START_REF] Gonizzi | Data dissemination scheme for distributed storage for IoT observation systems at large scale[END_REF], or applying data compression techniques such as those described in [START_REF] Marcelloni | A simple algorithm for data compression in wireless sensor networks[END_REF] [START_REF] Sadler | Data compression algorithms for energyconstrained devices in delay tolerant networks[END_REF]. Co mpression algorithms are suited for reduced storage and limited resources of ultra-s mall nodes (e.g. in [START_REF] Marcelloni | A simple algorithm for data compression in wireless sensor networks[END_REF] co mpression ratios up to 70% on environmental datasets).

C. Simulation results of localized dissemination 1) Non-segmented data item

Simu lation results of localized data item dissemination are shown in figures 14 and 15 for hop-counter (R=6) and probabilistic approaches (K=2.5), respectively.

Hop=2

Density=195/2500 Hop=6 Density=122/2500 As shown in figure 14, it can be concluded that the hop-byhop replication mechanism guarantees efficient data item storage in the target area of the material. The storage density is increased according to the hop-counter, as example, for Hop=2 data item is replicated in 195 nodes. However, for Hop=6, it is stored in 122.

As for probabilistic approach, the data is also efficiently replicated in the desired area as shown in figure 15. The density of storage is high in nodes around the master (i.e. the probability P is high in that nodes and it decreases away from the master).

1) Segmented data item

Simu lation results of localized segmented data item dissemination are shown in figures 16 and 17 for hop-counter and probabilistic approaches, respectively. The master node starts by disseminating the first segment. If the node memory is full, the node retransmits the packet without decrementing the counter. That is why the second segment is replicated outside the first one and the same for third segment as shown in figure 16. The last broadcasted item is replicated in large number of node than others. As example for Hop=6, the first segment is stored in 125 nodes, the second in 179 and the last segment is replicated in 267 nodes.

However, the segments are stored in the target area with the same replication rate using the probabilistic algorithm as shown in figure 17. All the segments have the same density. For this reason, probabilistic approach is more efficient than hop-counter for localized dissemination.

V. CONCLUSION

In this paper, data dissemination algorithms are proposed to store information in co mmunicating materials through embedded ultra-small wireless sensor nodes. A counter-based flooding approach coupled with storage strategies is used to disseminate the packets and to control the replication rate according to the importance level of the user information. Nonlocalized and then localized storage algorithms are studied. For each storage mode, hop-counter and probabilistic replication strategies are proposed. The performances of these solutions are evaluated through computer simu lation for non-segmented and segmented data. For non-localized dissemination, simulation results show that our probabilistic algorith m provides a more uniform replication than hop-counter-based which can more avoid data loss during physical material transformation. For localized dissemination, simu lation results show an efficient localized storage for non-segmented data using both the hop counter and the probabilistic approaches . However, probabilistic algorithm outperforms the other for segmented item as all the segments are replicated in the target area with the same density.

Fig. 1 .

 1 Fig. 1. Storage modes for data item dissemination in the material.

Fig. 2 .

 2 Fig. 2. Uniform dissemination for anticipation of material transformation.

1 Fig. 3 .

 13 Fig. 3. Storage density according to the importance level.

Fig. 4 .

 4 Fig. 4. Classification of data dissemination protocols in WSN.

Fig. 5 .

 5 Fig. 5. Flowchart of hoop-counter data dissemination process.

  importance level of the data item -MaxHop: the maximal number of hops needed to reach all the nodes of the materialF2: [0,1]→[0,1] I → P=I,With:-I: importance level of the data item

Fig. 7 .

 7 Fig. 7. Effect of the limitation function on the H0 value

Fig. 8 .

 8 Fig. 8. Non-localized hop-counter storage of non-segmented data item.

Fig. 9 .

 9 Fig. 9. Non-localized probabilistic storage of non-segmented data item.

Fig. 10 .

 10 Fig. 10. Division of the material into pieces of 50cm x50cm.

Fig. 11 .

 11 Fig. 11. Importance level versus standard deviation of storage strategies.

Figures 12 and 13 Fig. 12 .Fig. 13 .

 131213 Figures 12 and 13 show the simulation results of a segmented data item dissemination for the hop-counter and probabilistic strategies, respectively. Hop=2 Hop=3 S1=837 S2=702 S3=529 S1=623 S2=565 S3=478

Fig. 14 .Fig. 15 .

 1415 Fig. 14. Localized hop-counter storage of non-segmented data item. P=0.4 Density=24/2500 P=0.8 Density=95/2500

Fig. 16 .Fig. 17 .

 1617 Fig. 16. Localized hop-counter storage of segmented data item. P=0.4 P=0.8 S1=23 S2=28 S3=31 S1=103 S2=122 S3=105

TABLE I .

 I BROADCAST AND STORAGE APP ROACHES

			Broadcast mechanism	Storage strategy
	Probabilistic	approach	Probabilistic-based [8] [9]	Probabilistic-storage [17] [18] [20]
	Deterministic	approach	Counter-based [10] [11]	Hop-counter [19] [21]

TABLE II .

 II BROADCAST AND STORAGE APPROACHES

TABLE III .

 III SIMULATION PARAMETERS

	Material size	5mx 5m
	Number of nodes	2500
	Nodes density	100 nodes/m²
	Nodes distribution	Grid 50x50
	Location of master node	Center of the material
	MAC layer	TMAC
	Transmission power	-90 dbm
		CC2420 (Frequency= 20 MHz,
	Radio	Modulation= PSK, Throughput= 250
		kbps)
	Radio Collision Model	Additive interference model

In our article, the term segmentation is not employed in its classic meaning, usually used for the transport layer of the OSI model.


Kais Mekki
email: kais.mekki8@etu.univ-lorraine.fr

William Derigent
email: william.derigent@univ-lorraine.fr

Ahmed Zouinkhi
email: ahmed.zouinkhi@enig.rnu.tnnaceur.abdelkrim@enig.rnu.tn

Eric Rondeau
email: eric.rondeau@univ-lorraine.fr

Mohamed Naceur Abdelkrim

Keywords: wireless sensor network, communicating material, counter-based flooding, energy efficiency, network lifetime I

The communicating material is a new paradigm of Internet of Things. It is designed to perform efficient product control and ensure an information continuum all along the product life cycle. Communicating material is obtained by scattering a huge amount of microelectronic components such as sensor nodes. However, a critical aspect of application with wireless sensor networks is nodes lifetime. The major part of energy consumption is caused by communication module, therefore judicious power management and scheduling can effectively extend operational time. In this paper, the counter based broadcasting scheme is focused on and a random delay extension is proposed to improve the wireless sensor network lifetime for communicating material application. The extension need neither additional hardware nor extra overhead, so that the strength of the original counter based algorithm is maintained. Simulation results using Castalia/OMNeT++ showed that our delay extension can reduce the number of retransmitting nodes by about 10% and hence improve the material lifetime compared with the original scheme.

INTRODUCTION

Recent advances in micro and nano sensors, integrated circuit technology and low-power wireless communications enable the deployment of extremely small, low-cost wireless sensor nodes. Applications of wireless sensor networks (WSN) with high density nodes distribution include remote environmental monitoring, smart spaces, military surveillance, precision agriculture, and communicating material [START_REF] Mekki | Fault-tolerant and QoS based Network Layer for Security Management[END_REF] [START_REF] Gungor | Industrial wireless sensor networks: challenges, design, principles and technical approaches[END_REF].

The communicating material is a new paradigm of industrial information system and it was presented and discussed for the first time in [START_REF] Kubler | Problem definition methodology for the Communicating Material paradigm[END_REF]. It enhances a classic material with the following capabilities: it can store data, communicate the information at any point of the product, and keep these previous properties after physical modification. This technology leads to an important change in intelligent product. Indeed, the material does not communicate using some tags or nodes in specific points, but becomes intrinsically and continuously communicating. To meet this vision, many ultrasmall electronic devices (thousands) are scattered into the material of the product during its industrial manufacturing so as to be inseparable.

The first works focusing on communicating materials are presented in [START_REF] Kubler | Method for embedding context-sensitive information on "communicating textiles" via fuzzy AHP[END_REF] [START_REF] Kubler | Embedding data on "communicating materials" from context-sensitive information analysis[END_REF]. It introduces a communicating material (etextile) obtained by scattering a huge amount of RFID µtags (1500 tags/m²) in a manufactured textile. The system involves RFID reader/writer which is connected to a relational database that contains all the product life cycle information as shown in figure 1. At each writing operation, the database is explored to select the relevant data items that must be stored in the material. A Data item is a fragment of a database table, describing a particular data and requiring several bytes to be stored in the µtags when the textile passes under the writer module during the manufacturing phases. Since the RFID are memory-constrained, the data item is splitted (segmented) and stored over several tags using specific protocol header which is also able to rebuild the initial information.

Improvement of Counter Based Broadcasting Scheme for Long Communicating Material Lifetime

In a previous work [START_REF] Mekki | Multi-hop Counter Based Data Dissemination Protocol for Communicating Materials[END_REF], we have proposed to apply this system for solid and large materials such as concrete and wood using a multi-hop wireless sensor network technology as shown in figure 2. Ultra-small and micro-sensor nodes [START_REF] Mahfuz | A review of micro-nano-scale wireless sensor networks for environmental protection: Prospects and challenges[END_REF] [START_REF] Cao | Development of an integrated wireless sensor network micro-environmental monitoring system[END_REF] are dispersed into the material (100 nodes/m²). To store relevant data items in these scattered nodes, a dissemination algorithm is developed. It uses a counter named "Hop" which control the number of hops to do before storing the data. The packet is broadcasted from one node to all its neighbors, and at each hop the counter is decremented (Hop Hop-1) until it reaches zero. In this case, the node must store the data, then resets Hop to its initial value, and rebroadcasts the packet within its neighborhood. This process continues until the edges of material are reached. To broadcast the packet to all nodes of the material, the algorithm adopts a counter based forwarding scheme. When node receives the data packet, it fixes a random waiting delay. Then, it counts the number of retransmissions of the same packet by neighbor nodes. After the waiting delay has elapsed, the packet is only broadcasted if the number of retransmissions is smaller than a predetermined threshold.

However, the counter based forwarding scheme is resource blindness (i.e. node do not adopt energy saving scheme) [START_REF] Akdere | A comparison of epidemic algorithms in wireless sensor networks[END_REF]. Furthermore, the communicating material comprises multiple embedded ultra small wireless sensor nodes (thousands), each one uses very limited battery capacity and batteries replacement is impossible in such application. For these reasons, the energy consumption has to be optimized by reduction of the amount of transmitted data of each sensor node (i.e. wireless transmission consumes more energy than any other communication activity [START_REF] Hongping | Overview of data dissemination strategy in wireless sensor networks[END_REF]).

The contribution of this paper is to improve the counterbased broadcasting scheme by controlling the random delay according to the remainder energy of wireless sensor node. In this paper, this improvement is called "random assessment delay extension".

The paper is organized as follow. The following three sections describe firstly the original counter-based scheme. Then, an improved counter-based scheme is proposed. Finally, the simulation results and the performance evaluation are presented. The last section concludes the paper.

II. BROADCASTING SCHEMES

Broadcasting (diffusing a message from a source node to all nodes in the network) plays an important role in multi-hop wireless sensor networks. It is usually used for routing path discovery (e.g. DSR and AODV), network reprogramming, fault tolerance, data mules, and mobile sink management [START_REF] Sanchez | Performance evaluation of reliable and unreliable opportunistic flooding in wireless sensor network[END_REF]. Broadcast algorithms are usually referred to as flooding in which every node in the network retransmits an unseen received message once. However in dense sensor network, the flooding may cause serious redundancy, contentions and collisions which leads to high overheads and high energy consumption [START_REF] Kyasanur | Smart gossip: an adaptive gossip-based broadcasting service for sensor networks[END_REF]. To solve these problems, various efficient broadcast schemes have been proposed as shown in table 1. These schemes are usually divided into two categories [START_REF] Aminu | An Efficient Counter-Based Broadcast Scheme for Mobile Ad Hoc Networks[END_REF] [14]: deterministic and probabilistic. Deterministic schemes require global/local topology information of the network for rebroadcast decision. On the other hand, probabilistic one does not require this information as every node is allowed to retransmit a message based on a predetermined forwarding probability. Distance and location-based schemes exploit distance between nodes and their positions information, respectively. Such information is useful to reduce the amount of rebroadcasting. Nodes, however, need to be equipped with a Received Signal Strength Indicator (RSSI) or a Global Positioning System (GPS). Such additional functions increase the cost of nodes.

Neighbor-knowledge-based methods require that nodes exchange hello packets periodically to get the neighbors information. According to this information, the nodes can suppress unnecessary broadcasts. However, hello packet exchange incurs a large amount of communication overheads and energy consumption.

With cluster-based, the network is divided into groups of nodes. Such scheme require cluster formation phase using specific algorithms which incurs more energy consumption and overhead.

Probabilistic-based scheme define a value P. On receiving a forwarded message, node will rebroadcast it with a probability P. The scheme reduces more redundancy, contention and collision using small value of P. A random delay is inserted before rebroadcasting the message to differentiate the timing of transmission between neighboring nodes.

Counter-based scheme uses different method to reduce redundancy and contention. When node receives a new packet, it fixes a random waiting delay before making the forwarding decision. During this delay, the sensor counts the number of Communicating material (Concrete, wood,…)

Wireless sensor node don't store data item Wireless Sensor node store data item retransmissions of the same packet by neighbor nodes. After the waiting delay has elapsed, the packet is only forwarded if the number of retransmissions is smaller than a predetermined threshold. As a result, the forwarding ratio depends on the nodes density. In low density areas this ratio will be high, whereas in high density areas it will be low. Probabilistic and counter-based schemes reduce the amount of unnecessary rebroadcasts. This is achieved without additional hardware and without any control messages. Such features are desirable for micro-sensors networks and specially communicating material application because the cost of nodes can be reduced and energy resources can be saved. However, authors in [START_REF] Garbinato | Injecting powerawareness into epidemic information dissemination in sensor networks[END_REF] [START_REF] Izumi | Improvement of Counter-based Broadcasting by Random Assessment Delay Extension for Wireless Sensor Networks[END_REF] show that counter-based scheme outperforms the probabilistic one in terms of reachability (i.e. the percentage of nodes that are reached on average) and efficiency (i.e. the average amount of resources required for broadcasting a message). For this reason, the counter-based can be regarded as a promising broadcast algorithm for dense wireless micro-sensor networks and communicating material applications.

III. COUNTER-BASED BROADCASTING SCHEME

In simple flooding, the more duplicate broadcasted packets a node receives, the less effective its rebroadcasting becomes [START_REF] Downey | Evaluating the Impact of Limited Resource on the Performance of Flooding in Wireless Sensor Networks[END_REF]. This is because the duplicate messages are likely to have been received by its neighboring nodes. This fact is involved in the counter-based flooding. In this scheme, a node that has received redundant packet more than a predefined threshold Cth cancels the rebroadcasting. The counter-based algorithm is shown below:

1) When a node receives a broadcasted packet for the first time, the node initializes a counter N to one, and sets a random assessment delay (RAD) at a value between 0 and T max .

2) If the node receives the same broadcasted packet during the RAD, it increments the counter N (N N+1). If the counter reaches a preset threshold Cth, it cancels the rebroadcasting.

3) After the RAD expires, the node rebroadcast the packet to all neighbors. Increasing Cth gives a better reachability, but also reduces the benefits of counter-based algorithm (i.e. many retransmitting nodes) [START_REF] Jacobsson | An experimental investigation of optimized flooding protocols using a wireless sensor network testbed[END_REF]. The theoretical results in [START_REF] Machado | Data dissemination in autonomic wireless sensor networks[END_REF] suggest that Cth should be 4 or less for good performance in typical networks. Authors in [START_REF] Arango | Retransmission and Back-off Strategies for Broadcasting in Multi-hop Wireless Networks[END_REF] show that a threshold Cth set between 4 to 6 is preferable from the viewpoint of the trade-off between reachability and efficiency. Furthermore, simulation results in [START_REF] Izumi | Counter-Based Broadcasting with Hop Count Aware Random Assessment Delay Extension for Wireless Sensor Networks[END_REF] (presented first in [START_REF] Izumi | Improvement of Counter-based Broadcasting by Random Assessment Delay Extension for Wireless Sensor Networks[END_REF]) show that if the threshold is set to 4, the reachability is over 99.5%.

In [START_REF] Jacobsson | An experimental investigation of optimized flooding protocols using a wireless sensor network testbed[END_REF], the scheme is tested in a real wireless multi-hop network using t-mote Sky (based on Telos Revision B) from Moteiv Corporation, which is a sensor mote platform for extremely low power and high data-rate wireless sensor network applications. Authors showed that a threshold of 2 causes the reachability to suffer, while a value of more than 3 does not improve much. Instead, it only increases the number of retransmissions. They showed also that as T max became smaller, the retransmissions increased. And, when the RAD is 200 ms, there is very little improvement in the retransmissions if further increased. Using Cth=3 and T max =200 ms, the reachability was extremely good in dense sensor network scenario.

There are some research efforts to optimize and reduce the number of retransmitting nodes, hence improve the network lifetime for counter-based scheme (i.e. the major part of energy consumption is caused by communication module). In [START_REF] Chen | DISCOUNT: A Hybrid Probability-Based Broadcast Scheme for Wireless Ad Hoc Networks[END_REF], a node sets the value of the counter threshold Cth according to the number of its neighboring nodes (i.e. the local density). In [START_REF] Arango | Retransmission and Back-off Strategies for Broadcasting in Multi-hop Wireless Networks[END_REF], a node sets Cth according to the distance from the broadcasting node to itself. Similarly, in [START_REF] Neumann | Redundancy infrastructure for service-oriented wireless sensor networks[END_REF] and [START_REF] Gonizzi | Data dissemination scheme for distributed storage for IoT observation systems at large scale[END_REF], the RAD is a function of the distance from the broadcasting node. Moreover, author in [START_REF] Aminu | Performance Analysis of an Adaptive Probabilistic Counter-Based Broadcast Scheme for Mobile Ad Hoc Networks[END_REF] extend the scheme by adapting the RAD value to network congestion (i.e. different RAD values are used for each rate of packet reception).

These extensions, however, use network topology, neighborhood awareness, distance and location information to improve the scheme. Such improvements possibly diminish the strength of the original counter-based scheme (i.e. no additional hardware and low overhead). Furthermore, they don't assume the remainder energy level of nodes which could lead to the loss of important located nodes. This problem is the key contribution of our work.

In this current paper, the remainder energy is processed. The nodes with low energy level have to wait for large random delay RAD than others. So, they are prevented from rebroadcasting and hence improve their lifetime as much as possible. The counter-based broadcasting scheme has to maintain high reachability using our extension.

IV. PROPOSAL RANDM ASSESSMENT DELAY EXTENSION

The random waiting delay is processed in this paper to prevent as much as possible the nodes with low energy level from rebroadcasting. As consequence, the rate of rebroadcasting of each node is adapted to their remaining energy. In the original counter-based scheme, node selects random waiting delay RAD between 0 and a fixed period of time T max . To adopt this mechanism to remaining energy level of node, the equation 1 and 2 are developed. PET Proceedings -Copyright IPCO-2014 ISSN 2356-5608, pp.53-59

RAD extension = RAD x K K = (E initial / E remainder )
RAD extension : random assessment delay extension. E initial : initial energy of sensor node. E remainder : remainder energy of sensor node. RAD is multiplied by K which increases the waiting delay when the remainder energy becomes lower as shown in figure 4. If the remainder energy is very low, the waiting delay RAD extension is larger. So, the node has to wait more than others and count the number of retransmission N of neighbor nodes which have more energy. As consequence, it has more chance to reach the counter threshold Cth and cancel the rebroadcast which conserve more theirs energy.

V. SIMULATION AND PERFORMANCE EVALUATION

In this section, the simulation setup is described firstly. Then, the simulation results are presented and discussed.

A. Simulation setup

The performance of our solution is evaluated through simulation. Castalia simulator is used to implement the improved counter-based broadcasting scheme. Currently, many wireless sensor network simulators are available as COOJA and TOSSIM but Castalia provides realistic wireless channel, radio models and node behavior [START_REF] Sundani | Wireless Sensor Network Simulators, A Survey and Comparisons[END_REF].

In particular, Castalia has been configured to support communicating material application. A specific radio module is developed which is consistent with CC2420 components but allow more reduced transmission range. Thus, a thousand of scratted nodes in small material size could be simulated. The chosen simulation parameters are summarized in table 2.

B. Simulation results

1) Reachability and retransmitting nodes performances

In this section, the number of reached nodes (reachability) and the number of retransmitting nodes are used as performance metrics for the evaluation of our extended counter-based broadcasting scheme.

In order to verify the effect of our extension, simulation comparisons between the original scheme and the proposed one is performed. Different energy levels (Low E remainder , Medium E remainder , and High E remainder ) are randomly affected to sensors nodes in the material.

The counter-based scheme has two control parameters: Cth and T max .

First, T max is set to 10 second and the counter threshold Cth is varied. For each threshold, 30 trials with different random selection of RAD are executed and the average values of them are plotted in figures 5 and 6 for number of reached nodes and number of retransmitting nodes, respectively. As shown in figure 5, there is no difference in number of reached nodes. The reachability is over 99% for all counter thresholds. However, figure 6 shows that our proposed scheme reduces the number of retransmitting node by 10% because low energy level nodes don't rebroadcast packets. They have more waiting delay to count the retransmission of neighbor nodes (i.e. neighbors that have further residual energy) and have then [START_REF] Mekki | Fault-tolerant and QoS based Network Layer for Security Management[END_REF] (2) more chance to reach the counter threshold and cancel rebroadcast.

The larger counter threshold, the more retransmitting nodes and hence the more energy consumption. In the rest of the paper, Cth is set to 4.

Next, the value of T max is varied. For each value, 30 trials with different random selection of RAD are also executed and the average values of them are plotted in figure 7 and8. As shown in figure 7, the reachability is not influenced by T max for our proposed scheme. However, the number of reached nodes of the original counter-based drops in the case of too small T max (100 ms or less). This is because the broadcasted packets are transmitted by nodes during a short period, so they tend to collide each other and data don't reach a part of the communicating material. That is why also the number of retransmitting nodes drops in case of 50 ms of the original counter-based in figure 8.

The number of retransmitting nodes is significantly reduced with our scheme as the low energy level nodes doesn't retransmit packets as much as possible which optimize and balance the energy consumption of ultra-small nodes in the material. As consequence, the proposed solution outperforms the original counter-based in collision avoidance and energy consumption balance.

The longer T max , the less retransmitting nodes (less energy consumption) but the larger broadcasting delay in the communicating material. T max is set to 700 ms in the rest of the simulation study.

2) Energy consumption performance

In this study, all nodes have an initial energy equal to 18760 J. Broadcasting of different packets is simulated in the communicating material until the end of network lifetime. The average of consumed energy by all nodes is measured in different periods of time. The simulation result of the proposal and the original counter-based scheme are shown in figure 9. Figure 9 shows that there is no difference between the proposed and the original scheme in the beginning of network lifetime. However more time passes, our solution shows more energy conservation as nodes have different remainder energy and the lowest ones are not involved in broadcasting as much as possible. So, the random delay extension keeps a lot of energy and gives an equitable use of nodes resources (energy balance).

Therefore, our solution optimizes more the energy consumption and ensures a long communicating material lifetime (i.e. network lifetime) than the original counter-based scheme.

VI. CONCLUSION

In this paper, the counter-based scheme is focused on as a broadcast algorithm for communicating material using wireless sensor networks, and a random delay extension has been proposed to improve the original. The remainder energy level is processed to prevent as much as possible the nodes with low energy level from rebroadcasting which conserve more theirs resource. The performance of this solution is evaluated through computer simulation using Castalia/OMNeT++. Simulation results showed that our delay extension maintain the high reachability of the original scheme. However, it enhances the communicating material lifetime and reduces the number of retransmitting nodes by about 10%. The proposed extension can be applicable for any other broadcast algorithm which uses the random waiting delay.

Fig. 1 .

 1 Fig. 1. Data storage system for communicating textile.

Fig. 2 .

 2 Fig. 2. Data storage in communicating material using micro-sensor nodes.

Figure 3 Fig. 3 .

 33 Figure 3 summarize the algorithm of this scheme in each node. Algorithm: Counter-Based Broadcasting Scheme For a node X Upon reception of a broadcasted packet m for the first time -Initialize the packet counter N to 1 -Set and wait for RAD to expire -While waiting: o For every duplicate packet m received o Increment N by 1 -When the delay RAD expires -If (N<Cth) o Retransmit the packet m -Else o Drop the packet m End Algorithm Fig. 3. Algorithm of the counter-based broadcasting scheme

Fig. 4 .

 4 Fig. 4. Remainder energy versus the fraction Einitial / Eremainder.

Fig. 5 .

 5 Fig. 5. Counter threshold versus number of reached nodes.

Fig. 6 .

 6 Fig. 6. Counter threshold versus number of retransmitting nodes.

Fig. 7 .

 7 Fig. 7. Tmax versus number of reached nodes.

Fig. 8 .

 8 Fig. 8. Tmax versus number of retransmitting nodes.

Fig. 9 .

 9 Fig. 9. Counter threshold versus number of retransmitting nodes.

TABLE I .

 I BROADCASTING SCHEMES

TABLE II .

 II SIMULATION PARAMETERS

	Material size	5m x 5m
	Number of nodes	2500
	Nodes density	100 nodes/m²
	Nodes distribution	Grid 50x50
	Location of source node	Center of the material
	MAC layer	TMAC
	Transmission power	-90 dbm
	Radio	CC2420
	Radio Collision Model	Additive interference model