Ismail Salhi
email: isalhi@univ-mlv.fr

Erwan Livolant
email: livolant@univ-mlv.fr

Yacine Ghamri-Doudane

Stéphane Lohier
email: lohier@univ-mlv.fr

ZInC: Index-Coding for Many-to-One Communications in ZigBee Sensor Networks

Keywords: Wireless Sensor Networks, ZigBee, Index Coding, IEEE 802.15.4, Many-to-One communications, I

The main goal in wireless sensor networking remains the reduction of the network lifecycle and the enhancement of its reliability, keeping decent performances in terms of throughput and latency. Given the increasing interest of the research community on wireless network coding (NC), we think such challenges can be tackled using its innovative concepts, especially in the case of many-to-one communications where network coding has shown promising theoretical results. Yet, without a thoughtful adaptation to WSNs, the benefits of NC for sensor networking prove to be too "greedy" and impractical. In this paper, we propose index-coding, a simple and effective packet coding scheme that enhances significantly many-to-one communications in ZigBee sensor networks. Index-coding uses smart bit-shifting operations in order to encode short messages from a set of sensors to a sink using fewer transmissions. Our implementation in a real ZigBee testbed shows substantial enhancement of network performances and resiliency.

INTRODUCTION

The main objective of Wireless Sensor Networks (WSN) is to collect data from different sensors to one or multiple destinations. Except in multimedia sensing, the size of the reported data in such low-power/low-rate networks normally does not surpass few bytes per node, and packets are delivered using multi-hops communications that imply a substantial packet overhead. Reducing this overhead and the number of transmissions is one of the most important challenges in WSNs, as it allows alleviating the complexity of the node's communication stack and reducing its energy consumption, thereby prolonging the network lifecycle.

The network coding (NC) theory [START_REF] Ahlswede | Network information flow[END_REF] provides a set of coding systems that ensure a notable reduction of the number transmissions in wireless networks. An interesting way to tackle the inherent issues of WSNs is thus to make use of the concepts of this theory to improve existing communication protocols. Indeed, current advances in the applications of wireless NC have attracted the attention of scientists and system designers in computer networks to cope with the suboptimality of store-and-forward schemes. However, the application of NC to low-power/low-rate networks is not trivial. Indeed, an as is utilization of current coding methods such as [START_REF] Jafari | Compressed network coding vectors[END_REF]- [START_REF] Seferoglu | I2NC: Intra-and inter-session network coding for unicast flows in wireless networks[END_REF] in WSNs would be unproductive, if not impossible, since it involves a notable overhead and a negative impact on the memory intake of the communication stack 1 . This paper addresses the problem of many-to-one (M2O) or incast communications in ZigBee2 [START_REF]ZigBee Specifications[END_REF] networks and the underlying Medium Access Control layer, the IEEE 802.15.4 [START_REF]IEEE Standard for Information Technology, "Part 15.4: wireless medium access control (MAC) and physical layer (PHY) specifications for low-rate wireless personal area networks (LR-WPANs)[END_REF]. We present a lightweight yet effective packet coding scheme, so called ZigBee index-coding or ZinC, which improves many-to-one communications in terms of throughput, delay and energy efficiency. In M2O communications, several, probably correlated source nodes send their sensed information to a single receiver or sink. In this context, and considering such pattern, ZInC resides in a shim encoding layer that combines incoming packets from different sources into one outgoing packet following an index calculation based on the ZigBee hierarchical addressing. ZInC contrasts from other NC schemes because it is explicitly designed to run on constrained devices with very low memory and finite energy level. The principle behind ZInC is to convey the address of source nodes through an index they process using their own ZigBee addresses. Intermediate node can then XOR incoming packets, and generate a representation of the packet produced by each source node. This allows recovering the content of each combined packet and its source address at the sink.

To evaluate our solution, we measure in a real WSN testbed the impact of ZInC on different performance metrics central to sensor networks such as the number of transmissions, the endto-end delay and the energy intake. We show how ZigBee routers can deliver the same amount of data from a set of sources towards one destination with fewer transmissions, thereby improving throughput and latency with minimum overhead. We demonstrate how ZInC can operate within very constrained devices, and reveal the substantial benefits that index-coding may provide under realistic environments, namely, very low RAM memory, limited buffer management efficiency and unreliable channel conditions. Furthermore, we estimate in this experiential work that using ZInC can imply an energy consumption cutback up to 50% compared to classic ZigBee routing.

The rest of the paper is organized as follows: Section II discusses the related work in wireless network coding and the prior efforts in adapting its concepts to wireless sensor networking. Section III defines the context and the system's characteristics. In Section IV we detail our coding scheme and its main features, while Section V shows the practical benefits of ZInC via the detail of our implementation of index-coding in a real ZigBee sensor network testbed. The paper concludes with a brief discussion in Sections VI.

II. BACKGROUND & RELATED WORK

The utilization of network coding in wireless networks has shown notable results in terms of bandwidth utilization, reliability and latency [START_REF] Fragouli | Network Coding Applications[END_REF]. The original work of Ahlswede et. al. [START_REF] Ahlswede | Network information flow[END_REF] and all the follow-up works during the last 10 years have turned practical NC from a complex solution devoted to multicast networks [START_REF] Ho | A Random Linear Network Coding Approach to Multicast[END_REF], to a generic and effective scheme that mixes flows of packets to improve the performance of multihop wireless networks [START_REF] Fragouli | Network Coding Fundamentals[END_REF]. Today, practical NC is a whole research field by itself, and is studied for many applications such as satellite communications, large scale content distribution, delay tolerant networks or network security.

The first practical application of wireless network coding, namely COPE [START_REF] Katti | XORs in the air: practical wireless network coding[END_REF], has sparked the interest of researchers on using one-hop coding to improve wireless communications. Ever since, several protocols such as, CodeCast [START_REF] Fragouli | Network Coding Fundamentals[END_REF] or I2NC [START_REF] Seferoglu | I2NC: Intra-and inter-session network coding for unicast flows in wireless networks[END_REF] were developed and assessed on real testbeds using onehop XOR coding for unicast, anycast, multicast and dissemination over wireless networks [START_REF] Ishibashi | Topology and Mobility Considerations in Mobile Ad hoc Networks[END_REF]. Part of these efforts has been interested in the analysis of NC in terms of computational costs and coding efficiency. Other works have considered the coding reliability problem in lossy environments. Yet, few of these initiatives have been dedicated to the assessment of NC over realistic low-power/low-rate networks such as WSNs or WPANs. In fact, while NC has been analytically adapted to operate on WSNs in the literature [START_REF] Barros | The sensor reachback problem[END_REF]- [START_REF] Cui | Energy Efficient Opportunistic Network Coding for Wireless Networks[END_REF] and optimal solutions approached via heuristic-based schemes, none of the proposed solutions has provided conclusive answers to the practical problems that still prevent NC from implementable solutions (finite energy level, small buffer size, limited computational resources, etc.).

In [START_REF] Barros | The sensor reachback problem[END_REF], authors demonstrate that in a many-to-one network, the maximum available throughput can be achieved by finding a disjoint path from each source to the destination. Finding such solutions is hard, particularly under a dynamic topology and when the network is error-prone as it is the case in WSNs.

In their pioneering paper [START_REF] Ho | A Random Linear Network Coding Approach to Multicast[END_REF] Ho et al. induced briefly the potential of NC for multicast and a wider family of problems including the M2O communication in sensor networks. Since then, different versions of existing NC techniques were proposed to improve M2O communications. Still, since the performance analysis of most solutions is strictly theoretical, practical matters inherent to low-power/low rate networks like memory usage, real-time energy consumption or reliability are repeatedly untreated.

Authors of [START_REF] Toledo | Efficient Multipath in Sensor Networks using Diffusion and Network Coding[END_REF] propose a solution that significantly enhances the prior efforts on using NC in constrained networks. However, its performance evaluation emphases only the achievable throughput and the reliability in case of node failure, which, we think, makes their solution incomplete for practical application as energy consumption and deployment issues are not treated. In [START_REF] Wang | Partial Network Coding: Theory and Application for Continuous Sensor Data Collection[END_REF], the authors bring together the outcomes of the implementation of Partial Network Coding [START_REF] Jacobsen | Practical evaluation of partial network coding in wireless sensor networks[END_REF] (PNC) in order to remove outdated data without necessarily decoding any packet. Though PNC shows good performances under a real environment and proves that partial NC can be applied to low-memory and low-power devices. Its target remains application-specific. Indeed, PNC addresses the problem of efficiently discarding obsolete information in code-and-forward sensor networks which is one particular use case. Furthermore, the authors do not consider in their empirical analysis metrics central to WSNs like the distribution of the energy consumption, the latency or the number of transmissions. Similarly, authors of [START_REF] Hou | AdapCode: Adaptive Network Coding for Code Updates in Wireless Sensor Networks[END_REF] define AdapCode an interesting coding technique for data dissemination in WSNs where packets on intermediate nodes are linearly combined and decoded by Gaussian elimination. Although AdapCode provides promising results in terms of number of transmissions and reliability, it considers only dissemination-based networks, which is not the dominant traffic pattern in sensor networks. Besides, AdapCode does not provide any analysis on the energy consumption cost of NC.

In the most comprehensive work on energy-aware NC [START_REF] Cui | Energy Efficient Opportunistic Network Coding for Wireless Networks[END_REF], the authors propose COPR (Coding with Opportunistic Reception) a coding scheme that ensures decoding of each coded packet at its next hop. COPR is based on a backpressure algorithm that uses the reception of both coded and non-coded packets. It is important to know that COPR has not been intended for WSNs. It was designed to reduce the energy consumption of COPE in multi-hop wireless networks. Indeed, COPR was tested on IEEE 802.11b MAC layer which cannot be used in low-power/low-memory devices. Besides, while COPR ensures an energy cutback of almost 25% compared to XOR coding, it requires, on the other hand, the management of several queues for each unicast flow a node is involved in. This induces a large memory overhead that is incompatible with the limitations of state-of-the-art sensing devices. Moreover, in order to COPR to operate, the MAC addresses of all the onehop destinations have to be inserted in the coded data frame. This imply per frame, with the number of neighbors. Such overhead is small in an IEEE 802.11 data frame where the payload size equals 2312 bytes, but if we consider sensor networks MAC protocols such as IEEE 802.15.4 where the data frame does not exceed 118 bytes, the per packet coding overhead would be excessive.

In this paper, we define ZInC, a coding scheme that is, unlike other prevalent network coding solution, not based on packet eavesdropping, as we claim it implies a considerable energy consumption surplus and a significant memory overhead due to multiple buffers maintenance. ZInC enhances many-to-one communications in terms of latency, reliability and achievable throughput and ensures less power-greedy endto-end data delivery. It also differs from other coding schemes because it involves minor computational and network overheads. Thanks to the implementation of ZInC in a testbed, we show that it provides better performances and reliability than classic store-and-forward routing and an equitable energy consumption distribution across the routes toward the sink.

III. DESIGN CONSIDERATIONS

A. Motivation and Key Idea

The idea behind ZInC has emerged from our observation that the payload's size in WSNs is particularly small compared to other wireless technologies. For example, the payload of an IEEE 802.15.4 data frame is 20 times smaller than the payload of an IEEE 802.11 one. And the size of the sensed information is often considerably inferior to the available payload used to transmit them. Additionally, the required NWK and MAC headers are bigger than the effective payload itself. Indeed, in scalar WSNs, sources transmit data such as temperature, pressure or humidity. These can be coded in one message of few bits or bytes. For instance, to send the air temperature value, which we assume is a short integer, only 1 byte is sufficient. Yet, in classic WSN routing protocols like ZigBee or 6lowPan, such value would represent only of the payload and the NWK and MAC headers represent between and of its size. This suggests that classic NC schemes such as [START_REF] Katti | XORs in the air: practical wireless network coding[END_REF]- [START_REF] Seferoglu | I2NC: Intra-and inter-session network coding for unicast flows in wireless networks[END_REF] where the size of headers is function of the number of encoded packet must be avoided for overhead minimization. Moreover, these intrinsic characteristics to WSNs led us to think about a solution that takes advantage of the unused payload to improve the bandwidth utilization and the energy consumption. Indeed, this gap between the effective data and the carriage capacity of each packet makes it sub-optimal to transmit data in a store-and-forward manner.

B. System Assumptions

We consider a ZigBee scalar sensor network with the following configuration parameters [START_REF]ZigBee Specifications[END_REF] (

) with being the maximum number of children, the maximum number of children-routers and the maximum depth of the network. In ZigBee, M2O communications are performed using hierarchical-or mesh-based routing. Either way, we assume in this work that M2O communications occur between active nodes ( ) and the sink via their parent router through a route of hops (cf. Figure 1). We denote by the set of intermediate routers that constitute this route. Note that among the active nodes, is the number of active ZigBee routers and (

) the number of active end-devices (with ). We assume that sensed data of an arbitrary node are represented in a vector of size with .

In this context, we propose ZInC. A coding scheme that exploits the unused payload of forwarded packets to encode data from other source nodes in a decentralized way. Indeed, with index-coding, source nodes apply a bit-shift operation using their hierarchical ZigBee addresses. This shift allows intermediate nodes to perform a simple linear operation on the payloads of incoming packets, and transmit the whole data segment in one transmission instead of forwarding each packet independently. The sink retrieves then each source payload and its associated node address using reverse-index-coding. Note that ZInC is interesting only in the case of scalar sensor networking where the effective data are small enough to include them within the payload of other packets.

IV. ZINC OVERVIEW

The general principle behind ZInC is illustrated in Figure 1. Instead of forwarding incoming packets individually, the first intermediate router along the route to the sink bufferizes them during a predefined period of time. Then using index-coding, it combines their respective payloads in a single outgoing coded packet. This latter will then be forwarded to the destination for decoding. The aim of such coding technique is to lower the number of transmissions, reduce the end-to-end latency and improve the lifetime of routers involved in M2O communication by expanding their sleeping period.

A. Information Vector Transformation

To operate ZInC, active nodes ( ) apply a bit-shifting operation to their respective information vector before any transmission. This is done by processing an offset using the node's own ZigBee address as defined in equation 1. Where ( ) is the function that applies a -bits offset to .

( ) (1)

B. Local Index Calculation

The index (with ( )) used to know by how many bits is shifted has to be unique for each child node to avoid overlapping data when coding at intermediate routers. The value of is processed using the node's ZigBee address ( ). Equation 2 shows exactly how is processed. With ̇ being the designation of the direct parent of in the network hierarchy. And a configuration parameter in the ZigBee Tree Addressing algorithm [START_REF]ZigBee Specifications[END_REF]. Its value is processed by each node to reserve address space for their children. Note that nodes process locally and independently, without any centralized knowledge of the network or any control messages.

( ) { ( ) ( ̇) ( ̇) ( ̇) ( ) ( ̇) ( ̇) (2)

C. Packet Encoding

In order for the sink to recover all source messages, distinct coded packets must be generated by each linear combination at intermediate nodes. Indeed, as depicted in Figure 2, when a router receives packets from a subset of active source nodes ( ), it first queues them in its reception buffer . Then at a specific period of time, it linearly combines their pre-shifted payloads with its own one, into one packet (cf. Equation 3), and forwards it to the next upstream hop. Obviously, in the case where is not associated to any source, it simply transmits its packet .

As formerly stated, ZInC only addresses scalar WSNs where small amounts of data are reported. Figure 3 shows that ZInC can transmit up to 5 times less amount of data compared to ZigBee, specifically when the size of the sensed information is low ( bytes in this case 3 ). However as the size of data reports increase, the benefit of index-coding recedes because the pre-shifted source packets transmitted to the router become too large, thus, increasing the network overhead.

D. Packet Decoding

Whenever the destination receives an encoded packet, it applies a reverse index-coding operation. I.e., the sink extracts from the incoming packets (cf. Equation 4) the original vectors . Then, using the router source address field in the coded packet and each vector index , the sink processes the addresses of each original payload using Equation 5, and retrieves the identities of all the sources nodes involved in the communication. 3 We used in this numerical estimation a canonical topology in which 10 child-nodes report their sensed data to the sink via a 8-hops route.

( ) { ( ̇) ( ̇) ( ) ( ) ( ̇) ( ̇) ( ( ) ) ( ) (4)
The careful reader might notice that a special code needs to be defined to differentiate non-active nodes from active ones in coded packets. Indeed, in the case of an idle source, we use the vector as special code exclusively used when nodes do not transmit any data. This will prevent the destination from retrieving ambiguous information.

E. Canonical Example

For the sake of clarity, consider the canonical example depicted in Figure 4. In this part of the network, 4 of the 5 source nodes transmit 2-bits messages to their parent router . In this case, ZInC selects for each source node its corresponding shifted vector using ( ) . For example, it assigns columns to to the node . Node is assigned in the same way columns 2 to 4 and so on. V. PERFORMANCE EVALUATION In this section we show the performance of ZInC compared to classic ZigBee routing in a realistic scenario. We argue that simulation-based or analytical validation techniques are not appropriate for assessing architectures as technology-dependent and constrained as WSNs. Indeed, such impractical methods often overlook central properties like memory constraints, energy consumption and computational costs. Thus, to demonstrate that our solution is designed to run on real sensors using state-of-the-art WSNs communication protocols, we set up an experimental testbed (cf. Figure 5) that emulates practical many-to-one communications in a 802.15.4 environment.

We deploy 13 ZigBee sensors among which 5 are Memsic's MicaZ end-devices and 8 are TelosB routers. 9 sensors (5 end-devices and 4 routers) are potential source nodes that transmit sensed data at various constant bitrates (0.5 to 4 packets per-second). And 4 routers establish the route that forwards data flows to the sink. The parent of active sensors performs index-coding and forwards coded packets upstream. To mitigate randomness, all results are an average of 20 independent experiments. To the best of our knowledge, ZInC is the first practical coding implementation that deals with the incast (M2O) problem in WSNs and that provides conclusive answers in terms of both performances and WSN requirements.

A. Number of Transmissions

In a classic ZigBee network, intermediate nodes simply forward the messages transmitted by source nodes till they reach their destination. To transmit fewer packets, knowing that there is an under-exploitation of the bandwidth, ZInC allows each intermediate router to combine incoming messages from active sensors into one coded packet. Such coding would significantly reduce the number of transmissions as shown in Figure 6. Indeed, we first compare the average number of transmitted packets (including retransmissions) for each M2O communication using ZigBee forwarding or ZInC. Indexcoding outperforms ZigBee for a number of active nodes greater than 1 which is plausible since we address incast communications and not unicast. While the number of transmissions in ZigBee is linear with respect to the number of active sensors, the slope for ZInC is much smaller. Obviously the number of transmissions with ZInC is further reduced as the number of active sensors increase, because coded packets embed a larger number of packets.

B. End-to-End Latency & Reliabiltiy

An important metric that WSNs rely on is the per-packet endto-end latency. In sensor networks, the delay is strongly correlated to the loss rate endured by each link of the network. This is due to the use of the CSMA/CA carrier sensing scheme. Thanks to ZInC we can reduce the impact of loss rate on the latency, as nodes in this case transmit considerably less packets and thus reduce the probability of packet loss due to collisions. As plotted in Figure 7, the end-to-end delay using ZInC is slightly higher than with ZigBee when , which is due to the bufferization period needed to encode packet. However, when , the end-to-end latency provided by ZInC is almost 45ms lower than with ZigBee. Furthermore, the data delivery ratio show that ZInC delivers 3 times the amount of packets delivered by ZigBee as the number of active node increase and collisions occur more frequently.

C. Energy Consumption

The most important metric WSNs depend on is the energy consumption. Contrary to what one might think, the energy efficiency is not directly correlated with the number of transmissions, but rather with the time during which the communication module of the node is enabled. Indeed, [START_REF] Dunkels | Software-based on-line energy estimation for sensor nodes[END_REF] asserts that nodes that listen to the radio channel consume roughly the same amount of energy as those which constantly transmit packets. Thereby, to improve the energy efficiency of nodes, it is preferable to extend the radio modules inactivity time rather than seeking to reduce transmissions. ZInC allows to the routers involved in M2O communications to disable their radio module more often than ZigBee, since ZInC does not require from routers to relay every source packet to the sink. Instead, they only transmit one coded packet, and then deactivate the radio module till the next data reports. Figure 9 shows this energy consumption cutback. More importantly, ZInC allows a fairer distribution of the energy consumption across the network. Indeed, with ZigBee, routers closer to the sink are more solicited because they forward all source packets and have less sleeping time than other nodes of the network. Figure 10 shows how ZInC implies a better distribution in our testbed. One can see that routers (0x01, 0x02, and 0x03) consume around the same amount of energy as source nodes, since they forward the same amount of packets and then sleep till the next expected data reports.

VI. CONCLUSION

Throughout this work, we present ZInC, an innovative packet coding scheme designed for ZigBee sensor networks. Our implementation of ZInC in a real testbed proves that indexcoding significantly enhances the performance, the reliability, and energy efficiency of many-to-one communications.

Figure 1 -Figure 2 -

 12 Figure 1 -Nodes C 1 ,…,C a send their data to S via a k hops route either with ZigBee routing (in green) or via ZInC coding (in red)

Figure 3 -

 3 Figure 3 -Index-coding phases at an intermediate ZigBee router

,Figure 4 -

 4 Figure 4 -Index-Coding Illustration

Figure 5 -

 5 Figure 5 -ZInC M2O Experimental Testbed

Figure 6 -Figure 7 -

 67 Figure 6 -Average Number of Transmissions vs. Number of Sources

Figure 8 -

 8 Figure 8 -Number of Sources vs. Number of Delivered Packets

Figure 9 -

 9 Figure 9 -Energy Consumption vs. CBR BitrateTo assess the reliability of ZInC and ZigBee as the number of source nodes increases, we generate a CBR traffic that transmit 200 packets from all source nodes and process the number of received packets at the sink. Figure8depicts the results of this experiment and shows the number of delivered packets and the number of retransmissions. It is clear that ZInC outperforms ZigBee in terms of reliability. While ZigBee routing suffers rapidly from the increase of active sensors, ZInC ensures positive results and delivers almost 100% of the 200 packets.

Figure 10 -

 10 Figure 10 -Latency vs. Number of Sources vs. Data Delivery ratio The core idea behind ZInC emerged from the underexploitation of the payload in 802.15.4 networks. Indeed, thanks to index-coding, intermediate nodes can deliver in a distributed manner sensed data using the idle payload spaces at intermediate nodes and thus improve M2O communications.

If we consider the hardware used in our experiments, the size of the RAM is 10 3 -10 6 % smaller than in PCs or new generation cellphones.

ZigBee is the most widespread technology for sensor networking and wireless personal area networks (WPAN)


Soufiene Djahel
email: soufiene.djahel@ensiie.fr

Yacine Ghamri-Doudane

A Robust Congestion Control Scheme for Fast and Reliable Dissemination of Safety Messages in VANETs

Keywords: VANETs, Congestion control, IEEE 802.11P, Beacons, Safety messages

In this paper, we address the beacon congestion issue in Vehicular Ad Hoc Networks (VANETs) due to its devastating impact on the performance of ITS applications. The periodic beacon broadcast may consume a large part of the available bandwidth leading to an increasing number of collisions among MAC frames, especially in case of high vehicular density. This will severely affect the performance of the Intelligent Transportation Systems (ITS) safety based applications that require timely and reliable dissemination of the event-driven warning messages. To deal with this problem, we propose an original solution that consists of three phases as follows; priority assignment to the messages to be transmitted /forwarded according to two different metrics, congestion detection phase, and finally transmit power and beacon transmission rate adjustment to facilitate emergency messages spread within VANETs. Our solution outperforms the existing works since it doesn't alter the performance of the running ITS applications unless a VANET congestion state is detected. Moreover, it ensures that the most critical and nearest dangers are advertised prior to the farther and less damaging events. The simulation results show promising results and validate our solution.

I. INTRODUCTION

Vehicular Ad Hoc Networks (VANETs) [START_REF] Hossain | Vehicular telematics over heterogeneous wireless networks: A survey[END_REF] are new paradigm of wireless communications that aim to exploit the recent advances in wireless devices technology to enable intelligent inter-vehicle communication. VANETs are distinguished from other wireless networks by their specific characteristics such as; predictable vehicles movement and high speed, powerful processing units, large storage capacities and new applications scenarios. VANETs may also ensure wide dissemination of data and safety related information due to the large transmission range of vehicles and the specific routing protocols used like GPSR [START_REF] Karp | GPSR: Greedy perimeter stateless routing for wireless networks[END_REF], BROADCOMM [START_REF] Durresi | Emergency broadcast protocol for intervehicle communications[END_REF] and GEOCAST routing approach [START_REF] Maihfer | A survey of geocast routing protocols[END_REF]. Moreover, as compared to other wireless networks VANETs are not affected by strict energy constraints since the vehicle's battery can provide a long duration energy supply. Although, VANETs are unable to ensure connectivity between vehicles in certain circumstances like in rural areas where the network density is low. VANETs may also not guarantee timely detection of dangerous road conditions due to the high mobility of vehicles.

The purpose of this work is to design a congestion control mechanism that guarantees reliable and timely dissemination of safety related messages. Currently, most of the existing works propose to reduce the transmit power level as well as the frequency of beacon transmission to release more bandwidth for safety messages transmission, and thus prevent the occurrence of a congestion state. We believe that congestion prevention is not a good idea in VANETs, especially in the context of Hybrid Sensor and Vehicular Networks (HSVNs), as this leads to a severe degradation of ITS applications performance. As one of the main assets of VANETs is the proliferation of ITS applications for both safety and driving comfort purposes, it is not judicious to alter the performance of these applications (i.e, by reducing transmission power and beacon transmission rate) to prevent network congestion. To cope with this problem, we propose three stages based solution in which we first assign different priority levels to the emergency messages according to their contents and the number of hops that they have traveled. Secondly, we apply a congestion detection mechanism to identify any congestion state in VANETs. As a last stage, a vehicle adjusts its transmit power as well as its beacon transmission rate, according to the result of the previous step, to facilitate the dissemination of the emergency messages.

The remainder of the paper is organized as follows. Section II provides a brief description of the of IEEE 802.11P [START_REF] Jiang | IEEE 802.11p: Towards an International Standard for Wireless Access in Vehicular Environment[END_REF] functioning. Next, we present the most significant solutions for beacon congestion control in VANETs and highlight their weaknesses in Section III. In section IV, we introduce our congestion control scheme. In section V, we present and discuss the obtained simulation results. Finally, we conclude in Section VI.

II. OVERVIEW OF IEEE802.11 P

In order to provide an efficient means of communication in VANET and facilitate its integration with other networks, such as WSNs to constitute the so-called Hybrid Sensor and Vehicular Networks (HSVNs), the IEEE 802.11P task group has defined a set of specifications for Wireless Access in Vehicular Environment (WAVE) to fulfill the requirements of such challenging environment. The IEEE802.11P operates in the frequency band of 5.85-5.925 GHZ, within which the DSRC spectrum is divided to 7 channels of 10MHZ each. The control channel (CCH) is exclusively reserved for safety related communications like beacons and event-driven messages whereas up to six service channels (SCHs) are used for non safety data exchange. IEEE802.11P uses the same medium access mechanism of IEEE 802.11e, termed Enhanced Distributed Channel Access (EDCA) [START_REF]Wireless LAN medium access control (MAC) and physical layer (PHY) specifications: Amendment 8: Medium access control (MAC) quality of services enhancements[END_REF]. In IEEE802.11P, the channel time is divided into synchronization periods of 100 ms each, consisting of equal-length alternating CCH and SCH intervals. Therefore, the vehicle's devices must switch to the frequency of each channel (i.e, the CCH or one of the SCHs) during its specified interval in order to transmit the type of messages authorized during this period. To make this access scheme more accurate, a period equal to 4ms, called Guard time, is set at the beginning of each interval to account for the radio switching delay and the timing inaccuracies in the devices. Notice that the coordination between channels is achieved through the use of the Coordinated Universal Time (UTC) offered by a global navigation satellite system.

III. RELATED WORK

We say that the network is congested when the rate of the injected packets exceeds its processing capacity over a continuous period of time leading to an increasing number of packets loss.

Network congestion has been first studied in wired networks and some interesting solutions have been proposed to minimize its impact on network performance. The congestion problem is more severe in wireless networks compared to wired counterpart due to the broadcast nature of the wireless medium. Despite the fact that congestion control in wireless networks has been widely investigated it is still a hot topic that attracts much attention from the research community. In VANET, its specific characteristics, such as highly dynamic environment, frequently changing topology and distributed nature ..., render congestion control more challenging. So, we cannot apply the existing schemes for static wireless networks to this challenging environment.

Most of the proposed solutions to control the congestion in VANETs try to control the transmit power used for broadcasting the beacons to prevent the congestion state or at least alleviate its impact on the performance. This technique may cause, in some situations, an isolation of some vehicles when the network density decreases. This is due to the frequently changing topology of VANETs as the vehicles move very fast and change their directions so often.

Recently, some scholars have focused on designing reliable beacon congestion control mechanisms for VANETs. In what follows, we present the most significant contributions in the literature.

The automotive sector is considered as one of the main areas of concrete applications of Mobile Ad hoc Networks (MANETs). In VANETs, the topology changes within seconds and a congested node used for forwarding a few seconds ago might not be used at all at the point of time when the source reacts to the congestion. To take into account this special feature of VANETs, [START_REF] Wischhof | Congestion Control in Vehicular Ad Hoc Networks[END_REF] has proposed a new scheme in which each node locally adapts to the available bandwidth. The contribution of this paper is based on an utility function that calculates for each data packet a value representing the utility of transmitting this data packet at the current point of time. It proposes to assign data rates based on the average utility of data packets transmitted by a vehicle. Thereby, nodes transmitting information with a high utility for the VANET will be allowed to consume a larger part of the available bandwidth. This scheme requires that the nodes share the information that allows to each of them to calculate its own rate (i.e. the proportion of the available bandwidth that should use).

The scheme proposed in [START_REF] Torrent-Moreno | Vehicleto-Vehicle Communication: Fair Transmit Power Control for Safety-Critical Information[END_REF] highlights the importance of transmit power control to avoid saturated channel conditions and ensure the best use of the channel for safety related purposes. The goal of this work is to design a new transmit power control scheme that ensures distributed fair power adjustment for vehicular environments (D-FPAV) to control the load of periodic messages on the channel. This work seeks to achieve two objectives, as stated below.

• Make the bandwidth available for higher priority data like the dissemination of warning messages. • Treat the beacons received from different vehicles with equal rights. Beacon messages may contain the vehicle speed, its direction and its current position (if it is equipped with a GPS). A high load of beacon messages on the channel leads to huge increase of packets collision in CSMA/CA based MAC protocols. In this case, beacon messages will not be successfully decoded and warning messages will show a slow unreliable spread within VANET. The authors propose to use per packet -level interference management based on per packet transmit power control to give packets "relative" weights that control the introduced interferences and, implicitly, the ability to capture packets. They suggest to carefully controlling the load of beacon messages to prevent deterioration of the quality of reception of safety related information.

Other researchers have applied formal verification techniques to assess the effectiveness of their congestion control schemes rather than using the conventional simulation tools. A recent and interesting work was introduced in [START_REF] Fisher | Formal Analaysis of a VANET Congestion Control Protocol through Probabilistic Verification[END_REF], in which the authors have used the model checking technique to investigate the efficiency of the congestion control scheme proposed in [START_REF] Bouassida | A Cooperative and Fully-distributed Congestion Control Approach within VANETs[END_REF]. This scheme is based on a combined static and dynamic priority assignment schemes. The former scheme defines a message priority as a function of its content and the source application type. In another hand, the latter scheme uses some parameters regarding VANET context such as, surrounding vehicles density, vehicle speed and message utility. Using these priorities, each message is transmitted over an appropriate channel. To allow fast transmission of high priority messages, neighboring vehicles exchange information about the priority of the messages they sent. Thereby, transmission of low priority messages is delayed to prevent congestion. We conclude from the above description that the exchange of messages' priority information may quickly lead to congestion especially in highly dense VANETs such as in traffic jam scenarios.

IV. THE PROPOSED CONGESTION CONTROL SCHEME

In this section, we present the key principal and the different steps of our congestion control scheme. Figure . 2 gives a global overview of this scheme.

A. Priority assignment and messages scheduling

When a vehicle receives more than one event driven messages broadcasted by the Road-Side Units (RSUs), WSN gateways in case of HSVNs or sent by a neighbor vehicle as a result of collision, emergency braking ..., the MAC layer assigns to these messages different levels of priority according to their degree of importance and danger to set up a transmission order among them, especially in case of congestion.

The first metric used for safety messages scheduling is their content type. So, we can distinguish three types of safety message contents, as stated below. In our congestion control scheme, a vehicle that has more than one safety related messages waiting for transmission must first assign a priority to each message according to its content type as described above. In case of receiving many messages of the same content type a second metric is then used to determine the priority level of a given message. To take into account this metric, we add a field dubbed Hopcpt to the safety message packet header 1 as shown in Figure .  1. The value of this field is used to update the priority level of each safety related message. The priority level of a safety message decreases as the Hopcpt value increases. An emergency message is assigned the highest priority when its Hopcpt is equal to 1, which means that it is being transmitted from a direct neighbor vehicle, RSU or WSN Gateway. Therefore, it should be spread towards VANET as soon as the medium becomes free.

The primary purpose of this slight modification of the message header is to speed up the transmission of the fresh emergency messages at the expense of the old messages or those advertising a farther danger. This choice is due to the following reasons:

• A lower Hopcptvalue means that the danger is very close to the receiver vehicle. Thus, this message needs to be transmitted very fast towards its neighbors to prevent more damage. • A larger Hopcpt value indicates that the danger is relatively far from the receiver vehicle. Therefore, delaying its transmission is less harmful than the previous type of messages.

B. Congestion detection mechanism

How vehicles can detect that VANET is congested? To answer to this question, we should first define a set of metrics that represents VANET state at any point of time. After carefully studying VANET environment, we have chosen the following metrics:

• Average Waiting Time (AWT) to access the wireless medium (particularly the CCH) which can be also inferred from the Medium Busy Time (MBT). The MBT represents the time during which the wireless medium (CCH) was busy due to transmissions from the nearby vehicles. This gives an overview on the density of vehicles as well as the packets exchange rate among them. • Collision Rate (CR): this metric is defined as the ratio of the unsuccessful transmissions from the vehicle to the total number of sent packets over CCH.

CR(V ) =

Own unsuccessf ul transmissions ∑ sent messages over CCH [START_REF] Karp | GPSR: Greedy perimeter stateless routing for wireless networks[END_REF] To detect an unsuccessful transmission of a beacon message over the CCH, we may use one of the nearby vehicles as a collision detector and the sender vehicle carries out handshaking with it before broadcasting any beacon message. Therefore, any lost or collided beacon will be detected. • Beacon Reception Rate (BRR) that is expressed as the ratio of the number of received beacons, issued from different vehicles, to the total number of received beacons.

BRR(V ) = |N 1hop (V )| ∑ Beacons received(V ) (2)
where N 1hop (V) denotes the one hop neighbor set of the vehicle V . Each vehicle collects and updates the information regarding the above three metrics that express the state of VANET in terms of traffic load, at each Congestion Monitoring Interval (CM I). This interval is divided into a set of equal length mini-intervals. During each miniinterval one measurement is taken regarding the above metrics and the corresponding values are stored in a three dimensions vector called Congestion Index Vector (CIV ) CIVi = (AW Ti, CRi, BRRi) 1 One can argue that we can use the T T L field as a metric to realize the same task of this new metric, however our congestion control scheme is implemented at MAC layer where T T L value is not available. Moreover, different senders of safety messages may assign different values of T T L which makes the value of this field meaningless for our congestion control scheme. such that i indicates the i th mini-interval of current CM I. We consider that the sets of normal states (i.e, in which VANET load is normal) are aggregated close in the feature space while those of overloaded (congested) states are considered as a dispersed states that deviate from the cluster of the normal VANET states. According to this description of VANET's states, we perform the following computation to identify a congestion state. First, we use the set of collected information during a training CM I, that consists of M mini-intervals, to calculate the mean vector CIV following the formula given below:

CIV = ∑ M i=1 CIVi M (3)
Subsequently, we calculate the distance between the CIV measured during a given CM I and the CIV as follows:

Dist(CIV ) =∥ CIV -CIV ∥ 2 (4)
Finally, the congestion is detected if the distance is larger than a certain threshold T hr, as indicated in Equation. 5.

{ Dist(CIV ) > T hr VANET state is congested

Dist(CIV ) ≤ T hr VANET state is normal (5)
The T hr value is updated dynamically based on the information acquired from the messages broadcasted by the RSUs regarding the ahead traffic conditions, the messages received from the WSNs gateways in case of HSVNs (Hybrid Sensor and Vehicular Networks) context, the weather conditions as well as the traversed area (i.e, tunnels, intersections,...). Notice that the CIV values corresponding to a congestion states are discarded whereas those of normal states are used as a training measurement to determine the new CIV .

C. Adjusting the beacon load

When a vehicle ascertains that the network is congested in its vicinity it adjusts its beacon load in order to preserve some amount of the available bandwidth for transmission of the emergency messages that require low transmission delay. The beacon load can be reduced either through the reduction of the transmit power used to send out these beacons or by decreasing their transmission rate. We note here that usually wireless cards provide limited choices of power transmit levels to be used, and each of them corresponds to a certain transmission range within which any packet transmitted can be correctly decoded with high probability. In what follows, we present a scheme to adjust the transmit power and another one for beacon rate regulation.

1) Transmit power adjustment:

By analyzing the content of the received beacons, each vehicle maintains a neighboring table in which each entry consists of five parameters (vehicle id , speed, direction, expirationtime, T xpw), which are described as follows:

• vehicle id : identifier of the sender vehicle.

• speed: indicates the current speed of vehicle id .

• direction: determines whether a vehicle (vehicle id ) is moving in the same or opposite direction of the receiver vehicle. • expirationtime: is the duration after which if no new beacon is received from the same vehicle then the entry with the corresponding vehicle id is deleted. • T xpw: indicates the transmit power level used to transmit the received beacon. We assume that the vehicle is aware of (or it selects) the next forwarder of the generated/forwarded emergency message. To calculate an approximate value of the distance separating it to the sender of a beacon message, it uses the beacon Received Signal Strength (RSS) instead of the GPS information since this latter is not always available (e.g, the GPS signal cannot be received inside tunnels, areas characterized by high buildings ...). To adjust the transmit power for beacons, we calculate the new transmit power based on the minimum power used by the nearby vehicles, including itself, and the distance separating it to the next forwarder of the emergency message. Notice that the vehicle can determine this distance based on m other candidates to be next forwarder under the condition that they belong to the set N 1hop ∩ N old and m is determined based on the size of N 1hop . Here, N old denotes the previous set of one hop neighbors of the vehicle and N 1hop is the current set. We calculate the transmit power that the vehicle will use for subsequent transmissions according to the following equation.

P = M AX[min(T xpw(i), T xpw(own)), P (nf dist + δ)] ( 6
)
where δ represents the difference between the next forwarder distance (nf dist ) and the maximum distance (max dist ) separating one candidate to the vehicle. If max dist is smaller than nf dist then δ is set to 0. Notice that the value i refers to the vehicle id and P (nf dist + δ) can be interpreted as the transmit power that ensures a transmission range slightly greater than nf dist + δ.

In order to ensure fast dissemination of the emergency messages through the adaptation of the transmit power value to the change in the neighborhood and the CCH conditions, the P value calculated above is gradually increased or decreased as follows. First, we calculate the increase factor (IF ) according to the formula below.

IF = |(N 1hop ∪ N old ) -(N 1hop ∩ N old )| |N 1hop | (7)
Secondly, we adjust the transmit power P according to the IF value as described the following.

P 1 =                  P if IF = 0 P (1 + (1 -M IN (IF, BRR))) if 0 < IF ≤ 1
and CR is low

P (1 + ( (IF -1) IF * CR)) if 0 < IF ≤ 1
and CR is high

P (1 + ( (IF -1) IF )) Otherwise (8)
Finally, the transmit power level to be used for transmission is the minimum of the intermediate value P 1 and the current transmit power.

P = M IN [T xpw(i), P 1] (9)
If a vehicle has no message in the high priority messages queue it chooses a double backoff 2 value before transmitting its beacons or the lower priority warning messages. So, this vehicle gives more chances to its neighbors holding a high priority emergency messages to transmit them quickly. This extra delay is managed through the following equation.

Extra backof f = rand[0, Backof f × α] ( 10
)
Where Backof f is the currently chosen backoff value and α is a multiplicative factor equals to

1 |(|N old |-|N 1hop |)| if |(|N old | -|N 1hop |)| ̸ = 0,
otherwise it is set to a default value equals to 1. 2 double backoff means that the vehicle chooses twice a random backoff value, then it waits for the sum of both values before trying to transmit a message, if any. • learn the list of vehicles within its carrier sensing range (CSr): each vehicle analyzes the information contained in the received beacons and control messages of routing protocols to learn the set of vehicles within its CSr. Next, it extracts the maximal cliques set of the graph representing the topology within its CSr. • compute the bandwidth fair share (BF ): using the information acquired in the previous step, each vehicle can determine its bandwidth fair share. For the sake of brevity, the details of this step are omitted (the reader may refer to our previous work presented in [START_REF] Djahel | Characterizing the greedy behavior in wireless ad hoc networks[END_REF] to get a detailed description of bandwidth fair share estimation). Based on the calculated BF , the vehicle adjusts its beacon transmission rate as follows:

Brate = BF -B(emergency) Bsize ( 11
)
such that B(emergency) denotes the bandwidth portion reserved for the expected emergency messages. This value varies according to the same parameters used for updating the T hr value, discussed in section IV-B.

V. SIMULATION SETTING AND RESULTS

In this section, we present and discuss the obtained results that evaluates the performance of our congestion control scheme. We have Figure 3: Beacon delivery ratio under varying vehicular densities Figure 4: Emergency messages reception ratio under varying vehicular densities conducted our simulation using OPNET-16.0 [START_REF]OPNET Modeler[END_REF] which we have extended by adding new functions to the MAC layer component to be compliant with IEEE802.11P specifications. To highlight the effectiveness of our scheme in different VANETs conditions, we perform several simulation scenarios under various levels of vehicles density and emergency messages transmission rates. To run rational simulation scenarios, we have referred to some pioneers studies in the literature regarding VANETs' parameters configuration in real environments. So, we set the beacon transmission rate to 10 packets/s, which is a value that can provide accurate information to the safety components in VANETs as stated in [START_REF] Xu | Vehicle-to-Vehicle safety messaging in DSRC[END_REF]. We have also fixed the beacon size to 500 bytes since it is considered an acceptable value according to the study done in [START_REF] Raya | The security of Vehicular Ad Hoc Networks[END_REF]. The setting of the other parameters is summarized in the Table I.

In our simulation, we evaluate vehicular scenarios consisting of 10km bidirectional road section with four lanes. The vehicles move with varying average speed, from 60 km/h to 120 km/h, according to the vehicular density at a given point of time, which corresponds to a real vehicular traffic in many highways. Notice that we have used four traffic density levels (i.e, light, moderate, heavy and jam) to reflect the real situation different point of time during the day. In our scenario, the RSU broadcasts different types of messages including emergency, warning and information with various rates.

Figures 3 and4 depict the obtained results in terms of beacons delivery ratio (BDR) and emergency messages reception ratio (EM RR), under various traffic density levels. The BDR measures the amount of the broadcasted beacons that have been successfully received by the one hop neighbors of the sender, whereas EM RR refers to the portion of safety related messages that have been successfully advertised to the majority of vehicles. We can clearly observe from the two figures that both of BDR and EM RR are inversely proportional to the vehicles density levels. When the density level is light or moderate (i.e. from 10 to 30 vehicles/km/lane) both of our three schemes (i.e, power control, rate control and joint power and rate control) achieve higher BDR and EM RR compared to the ratio achieved when no congestion control is applied. For heavy vehicles density scenarios (i.e. from 40 to 60 vehicles/km/lane), VANET experiences a sharp decrease of BDR and EM RR when no congestion control is applied. In contrast, our three schemes still achieving acceptable ratios equal to 62% of BDR and 73% of EM RR. We remark here that this gain of EM RR is achieved at the detriment of the BDR since the higher priority assigned to the emergency messages and the smart adjustment of the transmit power increase the probability of their successful reception. Additionally, the portion of bandwidth devoted for emergency messages transmission by the rate control scheme will consolidate the previous probability, and meanwhile decreases that of beacons.

As the bandwidth fair share value, defined by our rate control scheme, depends solely on the topology of VANET within the CSr of the vehicle. So, its value varies whenever any change is occurred in the vicinity of the vehicle. Therefore, the corresponding Brate may be greater than the configured value in the simulation. In this case, our simulator will just transmit 10 beacons, however if it is smaller than 10 then the calculated Brate will be considered rather than the configured value.

We now compare the end to end delay incurred from the transmission of an emergency message to a 8 km faraway vehicle through VANET. As graphed in Figure . 5, the end to end delay varies from 35ms to 43 ms when no congestion control is deployed in VANET. However this delay is gets reduced when we apply our schemes and the highest reduction amount (equals to 20% reduction of the end to end delay) is achieved by the joint power and rate control scheme. We believe that this achievement encourages us to focus our efforts in the future to enhance the efficiency of both of the proposed schemes and find a better way to combine them together since the resulted scheme has shown the highest performance in the herein conducted simulation.

VI. CONCLUSION

A robust congestion detection and control scheme was introduced in this paper to overcome the drawbacks of the existing works in the literature. In our scheme, we have devised a three complementary stages based scheme that reduces the transmit power or the beacon transmission frequency only in case where congestion is confirmed. Thus, the performance of ITS related applications running on a given vehicle is kept reasonably high since it is altered only for relatively short congestion period. The working principal of our scheme has been evaluated through computer simulation and the obtained results have proven its efficiency.

Figure 1 :

 1 Figure 1: The safety messages' header

Figure 2 :

 2 Figure 2: Global overview on our congestion control scheme

Figure 5 :

 5 Figure 5: End to end delay of emergency messages transmission under varying vehicular densities


Soufiene Djahel
email: soufiene.djahel@ensiie.fr

Yacine Ghamri-Doudane

A Framework for Efficient Communication in Hybrid Sensor and Vehicular Networks

Keywords: VANETs, WSNs, HSVNs, Communication protocols

Fast transmission of event-driven warning messages and energy conservation are primary concerns to design robust Hybrid Sensors and Vehicular Networks (HSVNs). In last few years, several protocols have been proposed to address these issues. However, the tradeoff between energy consumption and latency has not been carefully studied and sometimes it is given higher priority than event detection efficiency which remains the first objective of HSVNs. Unlike the existing works, we propose a framework that provides equilibrium between the following three metrics of HSVNs; dangerous events detection, energy consumption and transmission delay. The main advantage of our framework is its ability to ensure an effective detection of dangers on the road and timely transmission of the corresponding warning messages towards the passing by vehicles. This is achieved through the proposed mechanism to switch the sensors' status between sleep and active modes as well as the devised communication scheme between WSN-Gateway and the vehicles cluster head. The preliminary simulation results confirm the effectiveness of our framework and encourage us to pursue further investigation to extend it.

I. INTRODUCTION

Vehicular Ad Hoc Networks (VANETs) [11] are new paradigm of wireless communications that aim to exploit the recent advances in wireless devices technology to enable intelligent inter-vehicle communication. VANETs are distinguished from other wireless networks by their specific characteristics such as; predictable vehicles movement and high speed, powerful processing units, large storage capacities and new applications scenarios. VANETs may also ensure wide dissemination of data and safety related information due to the large transmission range of vehicles and the specific routing protocols used like GPSR [START_REF] Karp | GPSR: Greedy perimeter stateless routing for wireless networks[END_REF], BROADCOMM [START_REF] Durresi | Emergency broadcast protocol for intervehicle communications[END_REF] and GEOCAST routing approach [START_REF] Maihfer | A survey of geocast routing protocols[END_REF]. Moreover, as compared to other wireless networks VANETs are not affected by strict energy constraints since the vehicle's battery can provide a long duration energy supply. Although, VANETs are unable to ensure connectivity between vehicles in certain circumstances like in rural areas where the network density is low. VANETs may also not guarantee timely detection of dangerous road conditions due to the high mobility of vehicles.

In contrast to VANETs, nodes in Wireless Sensor Networks (WSNs) [START_REF] Akyildiz | Wireless sensor networks: a survey[END_REF] are usually static, equipped with scarce energetic resources as well as low processing and storage capacities. Despite that, these sensor nodes are known by their high efficiency in detecting events. They ensure precise and persistent events detection and due to their small size they can be deployed inside road environments to monitor and report the road conditions.

According to the above discussion, we remark that VANETs and WSNs have complementary characteristics, so each of them can benefit from the strengths of the other network while compensating its weaknesses. As a consequence, a new concept of Hybrid Sensor and Vehicular Networks (HSVNs), that merges VANETs and WSNs, has been recently introduced and attracted a lot of attention from both industry and academia. The main goal of HSVN is to enhance the transportation safety through reducing the number and severity of accidents. Furthermore, it may also help the insurance companies as well as the police men investigation by providing useful information regarding the road conditions and vehicle speed at time of accident. We foresee that HSVNs will play a fundamental role in developing Intelligent Transportation Systems (ITS) applications and will be the corner stone for new set of applications that will appear as a consequence of this concept.

In HSVNs, the sensors are deployed along the road to monitor and gather information regarding the weather conditions (ice fall, rain), traffic congestion on a road segment, hidden obstacles, etc. Later on, the passing vehicles are warned about the detected danger if any, through the warning messages sent by the WSN sink (Gateway). The receiver vehicle (usually the leader of a group of vehicles organized in cluster) spreads these messages to the other vehicles using the well known dissemination protocols for inter-vehicles communication. As soon as the driver is aware of the danger, he will adapt his driving skills and speed to prevent or at least alleviate the accident fatalities. In order to contribute to the improvement of the efficiency of HSVNs, we mainly focus, in this paper, on the design of a framework that provides reliable and timely dissemination of the detected dangers from the roadside sensors towards the passing by vehicles. To this end, our framework must fulfill the following requirements:

• Ensure low delay and reliable transmission from WSN towards VANETs. • Design of energy efficient protocol for inter-sensors communication. • Propose flexible, scalable and cost effective architecture.

• The proposed architecture of HSVNs should be viable in real scenario. Due to pages limitation, we will give a global overview of our proposed framework along with a brief description of the functioning of each of its components. However, more details about each component will be presented in our future works.

The remainder of the paper is organized as follows. Section II gives an overview of the existing HSVNs' communication protocols. Next, we provide a detailed description of our framework in section III. In section IV, we present and discuss the preliminary simulation results. Finally, we conclude in Section V.

II. RELATED WORK

In last few years, some works have been conducted to design reliable and energy efficient communication protocols for HSVNs. However these works have some drawbacks, which we will highlight hereafter, that prevent them from ensuring efficient detection of dangerous events and timely transmission of corresponding warning messages to the vehicles (drivers).

Weingrtner et al [10] have described a prototype for HSVNs and addressed the different types of communication that may exist. Their work provides a global overview on HSVNs and addresses the main challenges that may arise as a consequence of this new architecture.

In order to investigate the feasibility of such network architecture, a field test has been conducted and shown very promising results. This work can be considered as a first step towards a deeper study and analysis to design solutions that guarantee reliable, low delay and energy efficient transmission from the event detector sensor to the WSN-Gateway, as well as fast dissemination of the warning messages within VANETs. Notice that the authors didn't provide any solutions to realize the aforementioned objectives of HSVNs.

A more detailed work has been done in [START_REF] Barba | Performance evaluation of a hybrid sensor and vehicular network to improve road safety[END_REF] by C.T. Barbara et all. The authors have proposed a communication protocol between the WSN-Gateway and the passing by vehicles. In this protocol, a two way connection should be established between the WSN-Gateway and the vehicles' cluster head before starting data transmission. This may decrease the time devoted for data transmission especially in case of high collision rate or if the WSN-Gateway sends some images regarding other intersections or road segments that the vehicle should cross to reach its destination. Furthermore, the data or images are exclusively sent to the cluster head vehicles, which may jeopardize the safety of drivers and passengers of other vehicles if an event is detected after the cluster head has left the WSN-Gateway transmission range.

Another architecture for HSVNs has been conceived in [START_REF] Qin | An integrated Network of Roadside Sensors and Vehicles for Driving Safety: Concept, Design and Experiments[END_REF] wherein a duty cycle scheduling scheme based on Time Division Multiple Access (TDMA) is used to reduce the transmission delay from a detector sensor to the WSN-Gateway. This scheme suffers from the same weaknesses of the previous one. Additionally, its main drawback is the periodic broadcast of beacons by the WSN-Gateway, which leads to quick depletion of its energy especially when the role of gateway is fixed. To optimize energy consumption, the authors suggest that the sensors stay in sleep mode and wake up upon request from the WSN-Gateway. This request is sent whenever an approaching vehicle is detected. Indeed, this will save the energy however some dangerous events that may occur during the sensors sleep time cannot be detected. These undetected events are useful to warn the drivers that a given area has recently experienced a danger (e.g., rock falls, animal crossing the roads ...), then they increase their vigilance in order to prevent any surprising events or at least alleviate their impact.

III. THE PROPOSED COMMUNICATION PROTOCOL IN HSVNS

In HSVNs, we distinguish three different types of communication, each of them follows specific rules and aims to accomplish a special task, as stated below.

• Inter sensors communication aims to ensure reliable event detection and fast event report to WSN-Gateway. • WSN-Gateway to vehicle communication is responsible for timely report of any detected event to the approaching vehicles which in their turn send back the received warning messages to other WSN-Gateways on the road to their destinations. So, this will allow redundant storage of warning messages and increase the prevention level. For example, the stored messages are used to warn the vehicles traveling in the opposite direction before they approach the dangerous area. • Inter vehicles communication ensures the dissemination of the warning messages gathered from WSN-Gateways to the whole VANET.

In our framework, the above communication types are not considered as autonomous components independents from each other, but they are designed to interact and complement each other. This way, the workload assigned to WSN is alleviated significantly, at the expense of VANET, and its lifetime is extended.

In what follows, we give a detailed description of the functioning of these communication types.

A. Sensors to sensors communication

We propose that all the sensor nodes are active during WSN initialization period, then a subset of them remains active and the rest go to sleep until they are requested to wake up again by the WSN-Gateway. This subset should cover the critical region that has recently experienced some events like ice fall, animals crossing the road, falling stones or rocks in mountainous areas, or simply in dangerous turn. A simple way for the WSN-Gateway to choose this subset is to keep a list L of the last N sensors that have reported dangerous events. Afterwards, if the frequency of reporting events from a sensor s is deemed to be high then the WSN-Gateway adds the two direct neighbors of s to this list in order to ensure timely detection of any event that may occur within the region around s. The purpose of increasing the number of awake sensors in this region is to prevent any surprising event that may suddenly occur when a vehicle pass by this road segment, particularly in rural areas, and threaten the driver and passengers safety. To summarize, we aim, through this design, to increase the safety level by predicting the possible danger that may arise through recording the historic of events occurred in a given road segment. Afterwards, we use this historic for prevention purposes.

In our Framework, a sensor node that has been requested by the WSN-Gateway to go to sleep will periodically (period = ρ µs) switch between sleep mode and medium sensing. That is, it stays for (ρ-1) µs in sleep mode, and subsequently senses the medium during 1 µs. If a signal is detected, then it becomes active for the subsequent ρ µs and remains so till it receives the frame sent to it. Therefore, when the WSN-Gateway wants to wake up a sensor it should send a packet whose the size is at least equal to ρ µs. Thereby, the wake up request will definitely be received by the intended sensor.

Another noteworthy feature of our framework, as compared to the works presented in section II, consists in sharing the burden of WSN-Gateway role between a predefined set of sensors, each of them is candidate to fulfill this role during the WSN lifetime. Those candidates accomplish the task of WSN-Gateway either periodically or following a predefined algorithm and for a fixed period of time. Due to the unpredictable events occurrence time and location as well as the random nature of vehicles arrival within the WSN-Gateway's transmission range, we have opted for an algorithm that takes into account the energy level of each candidate sensor and try to keep them operational within the WSN for long duration. Notice that when a candidate sensor becomes a WSN-Gateway, it receives the list L along with any new warning message that has been received from the passing vehicles. Thereby, the crucial information contained in these messages along with the list L are redundantly stored and can be easily recovered later. For more details, our sensor to sensor communication principle is summarized by the algorithm 1.

Since the warning message header shown in Figure 1 is exclusively added by the WSN-Gateway then the intermediate sensors relaying the detector sensor with WSN-Gateway forward a smaller packet. As a result, the transmission delay and the energy consumption are decreased.

Intra WSN transmission:

To reduce the end to end delay of warning messages transmission from a detector sensor towards the cluster head vehicle, we should first minimize the maximum delay induced from the transmission of this message from the event detector sensor to the WSN-Gateway. To this end, we propose to use directional antennas rather than the omnidirectional ones. This choice is motivated by the results of the study done in [9], where the authors have demonstrated that the use of directional antenna can extend the transmission range by a factor equals to

( 4 tan 2 θ 2 ) 2 α
, where θ is an angle representing the main beamwidth of the directional antenna and α is the path loss factor, as compared to the case of omnidirectional antenna. Hence, this allows us to monitor the events on a given road segment with less number of deployed Algorithm 1 Protocol functioning inside WSN 1: Initialization period : 2: For each sensor node s i set status(s i ) = active; 3: Upon reception of warning message from s i ; 4: Counter-event (s i ) ++ ; 5: At the end of the initialization period; 6: For each sensor node s i do the following; 7: if (Counter-event (s i ) > δ) then 8:

The WSN-Gateway adds s i to the list L; 9: end if 10: End of the initialization period 11: Periodic update of the list L by the WSN-Gateway : 12: Collect statistics about the detected events and the detector sensors; 13: At the end of each period P do as follows 14: For each sensor s i do 15: if (Counter-event (s i ) > δ) then 16:

The WSN-Gateway activates s i-1 and s i+1 if they are not already active; The WSN-Gateway activates s i-1 or s i+1 if it is not already active; 23: end if δ: this crucial value defines a threshold for the number of triggered events per sensor node. This threshold varies according to the road segment area, i.e rural, mountainous or urban. L: denotes the list of sensors that monitor a dangerous area that has experienced high rate of events. sensors, which leads to significant decrease of the transmission delay since this metric is proportional to the number of sensors separating a reporting sensor and the WSN-Gateway.

B. Vehicle to sensor and sensor to vehicle communication

Many ITS applications are built on two key messages: namely beacon and event-driven warning. Beacon messages are periodically broadcasted by each vehicle to inform its neighbors about its presence and status. In our framework, we use this beacon message, in addition to its previous role, as a notification of arrival from the vehicles' cluster head to the WSN-Gateway. This notification is transmitted via the IEEE802.15.4 interface of the vehicle since it is equipped with two interfaces (i-e, IEEE802.11P for Vehicle to Vehicle (V2V) communication and IEEE802.15.4 to communicate with the WSN-Gateway).

In order to shorten the connection establishment between the WSN-Gateway and the vehicles' cluster head we modify the format of the beacon broadcasted by the cluster head by adding the following fields:

• Its current speed (the field is calculated as multiple of 5, so we need 5 bits to represent a speed of maximum 130km/h). Notice that this modification aims at increasing the time devoted for transmitting useful information from the WSN-Gateway towards the cluster head and reciprocally. Therefore, a larger number of data bytes can be exchanged as compared to the schemes proposed in [START_REF] Qin | An integrated Network of Roadside Sensors and Vehicles for Driving Safety: Concept, Design and Experiments[END_REF] and [START_REF] Barba | Performance evaluation of a hybrid sensor and vehicular network to improve road safety[END_REF] which require a longer delay for connection establishment.

Upon reception of the cluster head's beacon, the WSN-Gateway starts counting down a timer equal to the "time av" value calculated in Equation. 1. Based on the cluster head vehicle speed, the WSN-Gateway calculates the available time for information exchange with the cluster head as follows:

timeav = (2 * Rtx/speedCH ) -CHDtime (1)
Where Rtx is the transmission range of the WSN-Gateway, the speedCH refers to the cluster head vehicle's current speed and CHDtime represents the cluster head detection time 1 . If the warning message advertising the new detected event is received by the WSN-Gateway during the period specified by the interval2 [t0 + CHDtime, t0+ CHDtime+ timeav[ then it can retransmit it towards the cluster head, as shown in Figure 2(a) where A(P1) and A(P2) represent the position at which the cluster head's vehicle is detected and that at which it is going to leave the WSN-Gateway transmission range, respectively. Otherwise, it is transmitted, as an urgent warning message, to any other vehicle (from the same cluster) which is within its transmission range, as depicted in Figure 2(b). The receiver vehicle (for example, vehicle B in Figure 2(b)) will disseminate the received message to all other cluster members including the cluster head. A passing vehicle (not a cluster head) distinguishes an urgent warning message from an ordinary one through the value of the field msg type in the message header, as depicted in Figure 1. If this vehicle receives a warning message whose the field msg type is equal to 0, it processes and forwards it to the vehicles behind it in the travel direction unless it has already done so (i.e, this message has been already spread through VANET).

To increase the reliability and the energy awareness of our framework, the warning messages received by the vehicles will be reinjected to other WSNs deployed on the nearby or faraway road segments. The objective behind this design is to provide a backup storage at different points on the road which allows the vehicles coming in the opposite direction to be notified earlier about the danger, if any. Thus, the driver has enough time to reduce the speed and change the direction if necessary. This design has multiple advantages, as stated below:

• Prevent costly transmission of the warning messages between large groups of sensors, so the energy is saved. • Sensors are deployed only in critical road segments, rather than wide deployments of WSNs which costs much. • The warning notification is available in multiple WSNs gateways which thus guarantees that all the passing by vehicles are aware of the danger, if any, even if VANETs lose connectivity when the vehicles move out of range, especially in rural areas. To prevent unnecessary messages processing and storage, when a WSN-Gateway receives a warning message from a vehicle, it first checks if it has been already received or not through comparison of its header with the headers of the stored messages. Moreover, to limit the number of stored messages the WSN-Gateway checks the field "Event sequence number" (see Figure 1) of the received message to ascertain that it contains recent information. If so, then the message is stored and the oldest ones among the stored messages are discarded if the storage capacity has reached its limit. Otherwise, it is discarded.

Algorithm 2 summaries the different steps of the interaction between the WSN-Gateway and the passing by vehicles.

C. Vehicle to vehicle (V2V) communication

V2V (vehicle to vehicle) communication is an emergent paradigm that necessitates careful analysis and design to fulfill the expected results. In our framework, the role of V2V communication is to ensure large dissemination of the warning messages. To this end, we use a cluster based routing protocol since the vehicles are already organized in clusters for their communication with WSN-Gateways. These clusters are created using the well known clustering algorithms designed for VANETs such as Clustering for Open IVC Networks (COIN) algorithm [START_REF] Blum | Mobility management in IVC networks[END_REF] and LORA CBF [START_REF] Santos | Performance evaluation of routing protocols in vehicular adhoc networks[END_REF].

As for MAC layer, the Wireless Access in Vehicular Environments (WAVE or IEEE 802.11P) protocol is used to allocate spectrum for vehicular communication. However, IEEE 802.11P does not provide sufficient spectrum for reliable exchange of safety related information (like event driven warning messages) among the vehicles. To overcome this weakness of IEEE 802.11P, we are currently exploring the possibilities of applying cognitive radio technology to increase the spectrum allocated to the control channel (CCH) by WAVE, where all safety messages are transmitted. Moreover, as a perspective of this work we plan to propose a robust congestion control mechanism that ensures fast dissemination of the event-driven warning messages received from the WSN-Gateways even in case of high density or traffic jam in the highways.

IV. SIMULATION RESULTS

Having described the main components of our framework as well as their functioning, we now proceed to measure its performance through a computer simulation using OPNET16.0 [12]. Simulation parameters and configuration of both sensors and vehicles are summarized in the Table I. In order to highlight the effectiveness of our framework, the following metrics have been evaluated.

• Dangerous Events Detection Ratio (DEDR): is the proportion of the detected events to the total number of the occurred ones. This metric is evaluated under the following scenarios.

-Scenario1: in which our framework is disabled and all the detector sensors are initially in sleep mode, they wake up upon request from the WSN-Gateway as stated in [START_REF] Qin | An integrated Network of Roadside Sensors and Vehicles for Driving Safety: Concept, Design and Experiments[END_REF].

Vehicles speed and HSVN beacon interval (BI) 3 are varied to highlight their impact on the DEDR. -Scenario2: in this scenario, we assess the impact of the threshold δ, which varies from 1 to10, on the DEDR metric of our framework. This impact is tested under three different periods P (see Algorithm 1) used by the WSN-Gateway to send wake up/sleep requests.

• We also measure the AWareness Degree (AWD) (under varying vehicles speed and beacon intervals) of the vehicles (drivers) regarding the danger on the road with and without our framework. The AWD is defined as the ratio of the number of warning messages successfully and timely advertised to the drivers to the total number of the detected events by the sensors.

We have simulated a scenario of a motorway with road segments of 10 km length each. This motorway is composed of 4 lanes (2 in each direction). In our simulation, we only focus on one road segment where 5 WSN groups are deployed, each of them covers a road length of 2 km. Events occur randomly in terms of time and location along the road segment. These events will trigger warning messages transmission by the detector sensor. The vehicles are moving along the motorway in both directions while the cluster head ones communicate with the WSN-Gateways of the WSN groups. Average vehicles speed is varied within the range of [25 m/s, 33m/s], thus the available time for exchanging data packets with the WSN-Gateways varies accordingly. The vehicles are equipped with both IEEE802.11 and IEEE802.15.4 MAC protocols since any vehicle can serve as cluster head during its travel and communicate with WSN. To create interferences, we deploy two fixed nodes, near each WSN-Gateway, equipped with IEEE802.11 interfaces and exchange packets of 1000 bytes at the rate of 50 packets/s.

Figure 3 shows the impact of cluster head vehicles speed as well as the beacon interval on the DEDR in baseline HSVN (i.e, HSVN in which our framework is not used). As we can see from the plotted histogram, the DEDR is relatively low since the highest value is equal to 55 % and it is inversely proportional to the cluster head vehicles speed and the beacon interval. We remark also that larger beacon interval (800 ms) leads to an important decrease of the DEDR value. The reason behind this decrease of the DEDR underlies beneath the fact that in case of high speed, the timeav decreases significantly and consequently the sensors have less time to detect dangerous events. Moreover, a large beacon interval increases the likelihood that the CHDtime gets larger, and thus the WSN-Gateway wake up requests are sent late. Additionally, All the events detected after the cluster head has left the WSN-Gateway transmission range are not transmitted to the rest of the cluster members. This is due to the fact that in the schemes presented in section II the cluster head is the only vehicle that communicates with the WSN-Gateway.

In contrast, as depicted in Figure 4 the DEDR value achieved by our framework is not affected neither by the variation of vehicles speed nor the variation of beacon interval, however the only factor that may affect it is the threshold value (δ) and the Period (P) (see Algorithm 1). The plotted curve reveals that the DEDR value decreases as the threshold value increases till it reaches its lowest value (42 %) when δ is equal to 10 and the Period is set to 300 seconds. This value represents a decrease of more than the half of DEDR as compared to the case when δ = [START_REF] Karp | GPSR: Greedy perimeter stateless routing for wireless networks[END_REF][START_REF] Durresi | Emergency broadcast protocol for intervehicle communications[END_REF][START_REF] Maihfer | A survey of geocast routing protocols[END_REF]. This sharp decrease is a consequence of the reduced number of active sensors in a WSN group since the large value of δ (i.e, 10) and the random nature of event occurrence within the road segment covered by a WSN group leads to lower event detection probability. Moreover, the Period value slightly affects the DEDR which depends mainly on the threshold value. The threshold value is then a crucial parameter since a low value leads to higher DEDR, however it causes also a significant increase of energy consumption of the WSN-Gateway as well as the intermediate sensors due to the large number of wake up requests sent, especially when the WSN size gets larger. On the other hand, a large value of threshold minimizes the energy consumption but jeopardizes our framework performance as many dangerous events are still not detected. Thus, there is a tradeoff between the desired performance and the energy consumption level, which is hard to manage in order to find an adequate threshold value that satisfies both of these requirements.

To summarize, from these two Figures ( 3 and4) we can clearly observe that our framework outperforms the existing schemes (in terms of DEDR value) since, in our framework, this metric is not affected by neither vehicles speed nor beacon interval and can achieve values close to 98 % in the best case, whereas that in the existing schemes achieves only a DEDR of 55 %.

According to the histogram plotted in Figure 5, we observe that, in contrast to DEDR, the AWD is affected by both vehicles speed and beacon interval values. A high vehicles speed reduces significantly the time available for data exchange, especially if lower data rates for IEEE802.15.4 are used (e.g, 20 or 40 kbps). The timeav is further reduced when the beacon interval gets higher (e.g. 800 ms or larger). Moreover, the interference caused by the nearby equipments leads to high collision rate and thus the number of warning messages successfully and timely transmitted towards the passing by vehicles is severely affected.

We now compare the AWD of a baseline HSVN with that of HSVN dotted with our framework. To this end, the interference level is reduced and the beacon interval is set to 400 ms to allow more accurate comparison. As we observe from the histogram graphed on the Figure 6, our framework outperforms the baseline HSVN under varying vehicles speeds since the gaps between them in terms of AWD is important. This supremacy of our framework is justified by the fact that in the baseline HSVN all the events detected after the cluster head has left the WSN-Gateway transmission range are not transmitted to the rest of the cluster members. In contrast, in our framework those events are immediately advertised to the next vehicle present within the WSN-Gateway transmission range, thus they are timely delivered to the drivers.

V. CONCLUSION

A framework for robust communication in Hybrid Sensor and Vehicular Networks (HSVNs) was introduced throughout this paper to overcome the drawbacks of the existing works in the literature. In our framework, we have provided a design guideline for HSVNs aiming to delegate the heavy tasks (in terms of energy consumption and computation) to the vehicles while alleviating the tasks performed by the sensors. Likewise, we have devised a sleep/active mode scheduling scheme, carried out by the WSN-Gateway, which requests a given sensor to wake up or go to sleep according to the assessed danger level of the area around it. So, this ensures high efficiency of events detection and saves the energy as it is consumed only if necessary. The performance of our framework was evaluated using computer simulations and interesting results were obtained in terms of both DEDR and AWD. As future research direction, we would like

Figure 1 :

 1 Figure 1: Warning message's header added by the WSN-Gateway

Figure 2 :

 2 Figure 2: WSN-VANETs interaction

Figure 3 :Figure 4 :

 34 Figure 3: Impact of the cluster head vehicle speed and the HSVN beacon interval (BI) on dangerous events detection efficiency (DEDR): case of baseline HSVN

Figure 5 :Figure 6 :

 56 Figure 5: Impact of the HSVN beacon interval (BI) and vehicles speed on the awareness degree (AWD): case of baseline HSVN

17 :

 17 Add s i-1 and s i+1 to the list L; 18: end if 19: if (Counter-event (s i ) == 0) then

	20:	The sensor s i is requested to go to sleep;
	21:	Delete s i from L;
	22:

  Algorithm 2 WSN and VANETs interaction 1: Upon reception of a beacon or warning-msg from a vehicle : 2: if (CH-beacon received) then

	3:	CH-beacon processing
	4:	Set timeout to time av
	5:	Decrease the timeout at each time slot
	6:	Prepare warning-msg, if any
	7:	Send warning-msg to CH-vehicle
	8: end if
	9: if (warning-msg received from CH-vehicle) then
	10:	if (it contains fresh information) then
	11:	Store warning-msg
	12:	else
	13:	Discard warning-msg
	14:	end if
	15: end if
	16: if (timeout reaches 0) then
	17:	Triggers cluster head left event
	18: end if
	19: Upon reception of a warning-msg from a sensor node
	:
	20: if (warning-msg received) then
	21:	if (cluster-head left) then
	22:	Set msg-type to 1
	23:	else
	24:	Set msg-type to 0
	25:	end if
	26:	Broadcasts the warning-msg towards VANETs

• Its Id (identifier) • The coordinates of its destination 27: end if CH -beacon: refers to the beacon msg sent by the cluster head vehicle (CH-vehicle), through the IEEE802.15.4 interface, to announce its arrival. We set msg-type to 1 to indicate that it is an urgent warning message because the CH-vehicle has left the WSN-Gateway transmission range (i.e, cluster-head left is true).

Table I :

 I Configuration of HSVNs parameters

	Parameters	Values
	Road length	10km
	Road lane length	3m
	Physical layer	Direct sequence
	Transmission range (vehicles)	250 m
	Transmission range (sensors)	80 m
	No. of sensors in each WSN group	25
	Inter-sensors distance	60 m
	No. of vehicles on the road	10...60
	Data rate (for vehicles)	5.5 mbps
	Data rate (for sensors)	250 kbps
	Simulation time	1 hour
	No. of simulation epochs	5

The cluster head detection time denotes the difference between the reception time of the CH-beacon by the WSN-Gateway and the arrival time of the CH-vehicle within its transmission range.

t 0 refers to the arrival time of the vehicles' cluster head within the transmission range of the WSN-Gateway.

the HSVN beacon interval represents the period separating two consecutive transmissions of beacon messages through IEEE 802.15.4 interface to announce the arrival of a vehicle.