Daouda Ahmat

Tegawendé F Bissyandé
email: tegawende.bissyande@uni.lu

Damien Magoni
email: magoni@labri.fr

Towards Securing Communications in Infrastructure-poor Areas

Keywords: P2P, Security, DHT, Key Management, Distributed Systems

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Opportunities of development are countless in a connected world. Unfortunately, participating to the information society remains a challenging endeavour for developing countries whose priorities lie elsewhere. Indeed, the lack of health care centers, schools, and practicable roads has pressing and damaging impact on the communities, outranking the need for investing in telecommunication infrastructures. In such contexts, opportunistic networking remains a prime choice to provide affordable means for sharing data, exchanging information and attempting to keep in touch with the rest of the world through internet [START_REF] Ouoba | Leveraging the Cultural Model for Opportunistic Networking in sub-Saharan Africa[END_REF].

Opportunistic networks are ideal for infrastructure-poor areas, where, contrary to traditional networking [START_REF] Ahmat | Muses: Mobile user secured session[END_REF] all nodes of the network are not deployed together, (2) the size of the network cannot be approximately predicted, and (3) even the locations of the nodes are not pre-designed [START_REF] Lilien | Opportunistic networks: Challenges in specializing the p2p paradigm[END_REF]. They constitute a special category of P2P networks where peers cannot rely on an 'observing' and 'mighty' central infrastructure whose existence is paramount. Instead, most implementations of P2P networks rely on a Distributed Hash Table (DHT) that provides a lookup service that each node in the network can use to map a given key with its corresponding value. Maintenance of the database of key/value mappings is obviously distributed among the nodes so as to avoid the use of a single point of failure that a central infrastructure would represent. Previous work has shown that it was possible to make DHTs scalable and reliable [START_REF] Risson | Survey of research towards robust peer-to-peer networks: search methods[END_REF][START_REF] Tiendrebeogo | Reliable and scalable distributed hash tables harnessing hyperbolic coordinates[END_REF]. Consequently, in the absence of a central infrastructure, structured P2P networks can still benefit from the properties of DHTs to allow scalable interaction among connected nodes. Nonetheless, in the context of such setups, security and privacy remains challenging to implement and maintain.

Without guarantee of provision of a central infrastructure, P2P networks are bound to face security and reliability issues that existing common policies and techniques fail to take into account in their implementations. For example, standard securization measures for communication involve the use of key-based encryption which is often implemented with public-key cryptography. However, a central problem with the use of such type of cryptography lies in confidence that a given public key is authentic, i.e., that it is correct and belongs to the entity claimed, that it has not been tampered with or that is has not been replaced by a malicious third party. Usually, this confidence is guaranteed by a central Public-Key Infrastructure (PKI), in which one or several certificate authorities certify ownership of key pairs. There is therefore a requirement for a strong centralization to manage cryptography keys, a luxury that P2P networks cannot practically afford [START_REF] Urdaneta | A survey of dht security techniques[END_REF].

We propose in this paper to take into account the specificities of P2P networks and the security and reliability requirements for exchanging information in this era, to design a new approach for the management of public keys. Contrary to the traditionnal PKI, the management of public keys in our scheme is decentralized. Indeed, this management is distribued among the peers that form the P2P system. In practice, we rely on the "hyperbolic plane"-based topology where each node can select his parent accordingly in a distributed process. Public keys can then be forwarded safely for use by peers to ensure communication security. In previous work [START_REF] Ahmat | Muses: Mobile user secured session[END_REF], we have proposed an approach to address the security issues of the communication sessions when users are mobile across the network of nodes. This approach targetting user-level applications is complementary to the approach developed in this paper which targets the security of the underlying network nodes.

The main contributions of this paper are:

-We first discuss the opportunities of P2P networks for developing regions: where and how they can be harnessed to deliver connectivity in a truely beneficial way. We then discuss the challenges that arise in such networks, focusing on the safety and reliability of communications. -We then discuss the challenges for implementing authentication of sources in a P2P network. We emphasize on why traditional PKI which are currently successful on the Internet appear to be inadequate for P2P systems.

-We detail our approach for distributing the management of public keys across the different nodes. The novelty of this approach is that it leverages existing principles in a state-of-the art topology, to provide a reliable and secure way to manage public keys.

The remainder of this paper is organized as follows. Section 2 discusses P2P networking in the context of African developing regions. Section 3 enumerates the challenges that must be overcome to secure communications in P2P networks. Section 4.2 presents our approach. Section 6 discusses related work and Section 7 concludes.

P2P and developing areas

The network environment in developing regions, in particular Africa, is challenged. Networks in such areas are often characterized by frequent, lengthy, and unpredictable link outages, as well as congested usage of an already limited bandwidth [START_REF] Demmer | A Delay Tolerant Networking and System Architecture for Developing Regions[END_REF]. A recent comparison of bandwidth available and its costs in developed and developing regions showed that the discrepancy reaches an order of magnitude. [START_REF] Saif | A peer-to-peer internet for the developing world[END_REF]. Mainly three reasons explain such differences:

(1) the cost of incoming bandwidth on links between developed and developing countries;

(2) the lack of performant and adequate ISPs at the right scale; and

(3) inadequate provisionning for "pre-paid" users who account for most internet users but whose base is harder to anticipate.

To address these issues, researchers and practitionners have relied on alternative paradigms involving Delay Tolerant Networks [START_REF] Demmer | A Delay Tolerant Networking and System Architecture for Developing Regions[END_REF] and Opportunistic Networks [START_REF] Lilien | Opportunistic networks: Challenges in specializing the p2p paradigm[END_REF][START_REF] Lindgren | The quest for a killer app for opportunistic and delay tolerant networks (invited paper)[END_REF][START_REF] Ouoba | Leveraging the Cultural Model for Opportunistic Networking in sub-Saharan Africa[END_REF][START_REF] Reich | The age of impatience: optimal replication schemes for opportunistic networks[END_REF] which appeared to suit the requirements of developing regions. In this context, Peer-to-peer systems have been proposed to support the enhancement of connectivity across developing areas. Researchers have thus proposed to apply P2P technologies to networking needs that are more urgent than simple (and often illegal ?) file sharing. For instance, P2P was found suitable for offline internet access [START_REF] Saif | Internet for the developing world: Offline internet access at modem-speed dialup connections[END_REF][START_REF] Saif | Poor man's broadband: Peer-to-peer dialup networking[END_REF].

Challenges

Securing communication in P2P networks is a challenging endeavor, especially with regards to the standard practice of crypting information. In this section, we precisely detail some obvious and non-obvious challenges to highlight the constraints of finding a solution for P2P systems.

Key distribution. The first challenge that we encounter is the mode of distributing cryptographic keys in infrastructure-poor or infrastructure-less areas. Indeed, traditionally, a management-friendly central infrastructure, called PKI, is relied upon for this task. In absence of infrastructure we propose a fully distributed approach to spread keys. To this end, we benefit from the distributed features of existing "hyperbolic plane" overlay networks, in particular self-organization.

Use of alternate infrastructure. Relying on the hyperbolic tree to assure the forwarding of cryptographic keys also comes with problems that traditional PKI was able to easily handled. Indeed, there is a new need to ensure in a distributed system that the construction of the overlay network where each peer is properly identified will guarantee the robustness of the exchange scenario with little possibility of corruption by any intermediate peer.

Exchange of keys When initiating a communication in a P2P network, there will be a need for the peers to agree on the generation of a session key that the two peers will shared. A challenge in this requirement lies in the negotiation which should also be secured. Since usage of cryptography asymmetric algorithm to secure information is expensive, we propose to only rely on it in the key negotiation phase.

Detection of attacks

The last but note least issue is to provide a mechanism for detecting corrupted keys. Indeed, when a corrupted cryptographic key is detected, it should be revoked by peers who are aware of the corruption. The information must also be broadcasted in the network through a notification message that should assure that all corrupted keys are flushed out of the memory of connected peers. Similarly, a challenging endeavor will be to prevent the Manin-the-middle attacks during session key negotiation phase

System Design

In this section we discuss the different roadmaps that could be used to secure communications in P2P networks. We then detail our approach and the involved algorithms and heuristics.

Roadmap

In the absence of the traditional PKI for a central management of public keys, different schemes have been implemented to allow two peers to share a key (i.e., a secret) in a reliable way. We propose to go over those methods so as to highlight their limitations. We build our approach on top of these schemes, aiming at adressing with a DHT the different issues that we encounter in them:

• Diffie-Hellman (DH) protocol [START_REF] Diffie | New directions in cryptography[END_REF] The DH protocol is a strong cryptographic algorithm that enables sharing a secret between two nodes. Indeed, the proposed key exchange method allows two parties that have no prior knowledge of each other to jointly establish a shared secret. The strong point of the algorithm proposed by Diffie and Hellman also lies in the fact that the key exchange can be performed over an insecure communication channel. This protocol has been demonstrated to be secure against eavesdropping, and an extended version of the DH protocol has been implemented by Steiner et al. to share a secret in a group of n participating nodes [START_REF] Steiner | Diffie-hellman key distribution extended to group communication[END_REF].

Unfortunately, the DH scheme has been demonstrated to be opened to Manin-the-middle (MITM) attacks, a category of attacks that should be ruled out in P2P networks. • DH-based multipath key exchange method [START_REF] Takano | Multipath key exchange on p2p networks[END_REF] To address the MITM attacks with the DH protocol, Takano et al. have proposed a multipath key exchange scheme that enables to forward different components of an encryption key through separate paths, in order to prevent MITM attacks. Their approach is focused on P2P networks using a ring topology, such as Chord [START_REF] Morris | Chord: A Scalable Peerto-Peer Lookup Service for Internet Applications[END_REF] and Symphony [START_REF] Manku | Symphony: Distributed hashing in a small world[END_REF], and uses clockwise/anticlockwise routing technique. Nevertheless, despite its interesting features, this approach still suffers a few issues:

-it is restricted to a specific category of P2P networks (Chord and Symphony), and the ring topology does not appear to be realistic in the scenario of opportunistic networking where P2P connections are ad-hoc. -it makes various assumptions that cannot be guaranteed in a truely P2P opportunistic network.

An enhanced multipath DH-based key management

In this section, we propose an improvment (extension) of the model designed by Y. Takano et al. [START_REF] Takano | Multipath key exchange on p2p networks[END_REF]. Scalable, decentralized and self-organized networks such as DHT-based P2P systems enable many users to join them. Thus, each node can be connected to a lot of other nodes; there can be then several paths between two endpoints of the network. For the above reasons, network topology can be transformed into a graph: each node represents an edge of the graph and each link indicates a connection between two nodes. Considering that P2P networks consist of a large number of peers that have multiple connections with several peers, network topology could be represented by a p-connected graph.

Hyperbolic Addressing Tree. Our approach leverages previous work [START_REF] Cassagnes | Overlay Addressing and Routing System Based on Hyperbolic Geometry[END_REF] where the authors have demonstrated that hyperbolic geometry can be efficiently used for characterizing the overlay in a P2P network. Figure 1 thus illustrates the hyperbolic plane where the area is subdivided in disjoint zones allowing for each node to be positionned in a unique way while being able to be connected to a finite number of other nodes. This number corresponds to the degree q of the spanning tree that is thus built.

In the hyperbolic plane, a node of the tree can independantly compute the addresses corresponding to its children in the tree, and the degree of the tree determines how many addresses each peer in the network will be able to give. Thus by fixing the degree of the tree at the birth of the overlay, and by allowing each peer to connect to any peer at any time, the network can scale. In their As the global knowledge of the overlay is not necessary in their approach, "a new peer can obtain coordinates simply by asking an existing peer to be its parent and to give it an address for itself. If the asked peer has already given all its addresses, the new peer must ask an address to another existing peer. When a new peer obtains an address, it computes the addresses (i.e hyperbolic coordinates) of its future children. The addressing tree is thus incrementally built at the same time than the overlay" [START_REF] Cassagnes | Overlay Addressing and Routing System Based on Hyperbolic Geometry[END_REF]. For more details on the creation of the addressing tree, we refer the user to the work of Cassagnes et al. [START_REF] Cassagnes | Overlay Addressing and Routing System Based on Hyperbolic Geometry[END_REF]. We modify the integration of a new peer to the overlay to allow :

-a more careful selection of the parents so as to allow the existence of redundants paths that do not immediately interset at a parent node of a given source node. -a flexible construction of a P2P network that takes into account the security priorities of each connecting peer.

In our approch explicited by Algorithm 2, since every peer can connect to any other peer, a new peer can be forced to pick parents with specific properties when security is an important requirement for him. In our scheme, each peer must have at least two parents when it is concerned with security issues. Thus, while the redundant paths will allow to protect communication against eavesdropping, a selective connection will mitigate MITM attacks. Indeed, during the connection of a peer, each contacted potential parent will reply with the address it offers to a child as well as the addresses of the peers to whom it is already connected. Thus, the new peer can realise if this potential parent is also linked to his already selected other parent. In such cases, if the new peer requires very secure communications that will be MITM attack-proof, it must contact a different parent.

Key Exchange Method

Although the DH crytographic algorithm [START_REF] Diffie | New directions in cryptography[END_REF] has been widely used to share secrets on unsecure communication channels, it is MITM attacks. In order to overcome this limitation of DH-based scenarios, we propose to use a multipath key exchange scheme. Building upon a scalable P2P overlay [START_REF] Cassagnes | Overlay Addressing and Routing System Based on Hyperbolic Geometry[END_REF] which uses virtual coordinates taken from the hyperbolic plane that is indifferent to underlying P2P network topology, our key exchange mechanism is based on multipath key negotiation as described in Algorithm 4. Takano et al. [START_REF] Takano | Multipath key exchange on p2p networks[END_REF] have previously proposed a similar key negotiation mechanism. However, unlike our approach, their method is restricted only to Symphony and Chord P2P networks with a ring topology.

Algorithm 4: Key negotiation mechanism p : a prime number g : a generator 1. Alice selects a random number n, and computes Keya = g n (mod p). 2. Alice selects q random numbers ka0, ..., kaq-1, such that Keya = q-1 k=0 ka k (mod p).

3.

Alice routes all ka k to Bob via q potential separate paths 1 . 4. Bob receives q Keya's components ka0, ..., kaq-1 sended by Alice and computes Keya = q-1 k=0 ka k (mod p). 5. Bob selects a random number m, and computes Key b = g m (mod p). 6. Bob chooses q random numbers kb0, ..., kbq-1, such that Key b = q-1 k=0 kb k (mod p). 7. Bob sends all kb k to Alice via q potential separate paths 1 . 8. Alice receives q Key b 's components kb0, ..., kbq-1 from Alice and computes Key b = q-1 k=0 kb k (mod p). 9. Alice computes Key = Key b n = g n•m (mod p). 10. Bob computes Key = Keya m = g n•m (mod p).

Method for Dispatching Splitted Key Components

When a source node s is attempting to exchange a key with a destination node d, it splits this key into several subkey components that must be dispatched through separate channels. Algorithm 1 illustrates how the dispatching of key components is implemented. We propose a straightforward technique to overcome the challenge of selecting the different paths that are necessary to route separately the each subkey towards the destination node.

As described previously, in the tree representing the nodes of our overlay network, the tree degree is set and known from start, and each connected node has a degree that may be less or equal to the tree degree. Thus, given n the tree degree, each node wishing to forward a key must split it into n -1 components that will be routed in separate paths. However, the selection of paths is iterative to account for the possibility that a few nodes may have less connections than the maximum possible. Thus, each node numbers the different nodes it is connected to and considers each of these nodes as the begining of a different path. Thus when a node has a degree k, it sends the first component to its first connected node (whether a parent or a child), and the second component to the second connected node, and so on until all connected nodes are used, and then it comes back to the first node to send the (n -1 -k)th component. We formally describe this path selection in Algorithm 3.

Security analysis

In a multipath key exchange scheme, a malicious node that wishes to compromise a key being exchanged must be able to collect each and all subkey components routed in the network. Formally, when paths P 0 , ..., P q-1 are used to send several distinct keys from source S to destination D, the only malicious nodes that could compromise the key should be located at the intersection of all paths, i.e. M = P 0 ∩ ... ∩ P q-1 . S and D are obviously ignored in this set. Thus, when P 0 ∩ ... ∩ P q-1 = {S, D} (bigon criterion is respected [6, Lemma 2.5]) then all paths are disjoint and any MITM attack attempt cannot succeed. In such a desirable case, there exists a q-connected subgrah between S and D in the network topology. Consequently, the probability to have a MITM attack is estimated by σ = |P0∩...∩Pq-1|-2 |P0∪...∪Pq-1|-2 (where each path P i is constituted of a set of consecutive hops from source to destination, and 2 represents the source and destination nodes, i.e |{S}| + |{D}|).

The number of distinct paths is dependant on the source node degree. Thus, for a given q-regular tree, if q is a large number, then there is a very high probalitiy to have several disjoint transmission channels. Nonetheless, despite the robustness of our multipath negocation approach, cooperative MITM attacks, where several nodes maliciously cooperate to compromise a key, are possible. However, it is very hard, and excessively costly to execute such an attack in a real environment, especially in infrastructure poor areas.

Related Work

Previous work have proposed to use DHT infrastructure to manage cryptographic keys with more or less success and mostly with many caveats [START_REF] Kwon | The secure routing mechanism for dhtbased overlay network[END_REF][START_REF] Wang | Myrmic: Secure and robust dht routing[END_REF]. Wen et al. [START_REF] Wen | A key management mechanism for dht networks[END_REF] have proposed a key management mechanism for DHT networks that transforms DHT table entries into a tree structure [START_REF] Kim | Simple and fault-tolerant key agreement for dynamic collaborative groups[END_REF][START_REF] Lee | Distributed collaborative key agreement and authentication protocols for dynamic peer groups[END_REF]. Srivasta and Liu have relied on the Diffie-Hellman algorithm to deliver a solution that prevents threats in DHT networks [START_REF] Srivatsa | Vulnerabilities and security threats in structured overlay networks: a quantitative analysis[END_REF]. Their scheme, however, remained sensitive to Man-in-the-middle attacks.

Wang et al. have built a distributed PKI on top of the Chord structured overlay network [START_REF] Avramidis | Chord-pki: A distributed trust infrastructure based on p2p networks[END_REF]. They have used threshold cryptography to distribute the fonctionnality of the PKI across the nodes of the DHT network. This Chord-PKI provides traditional PKI features such as certification, revocation, storage and retrieval.

Takano et al. have proposed a Multipath Key Exchange similar to that proposed in our work [START_REF] Takano | Multipath key exchange on p2p networks[END_REF]. Their techniques however were designed to fit the Symphony and Chord P2P systems and their ring topologies.

Conclusion

Scalable P2P networks are self-organizing, autonomous systems that accept any peer without the need of resorting to a central coordination. Each peer alternatively plays the role of a router in order to relay traffic to other peers. The flexibility of such settings thus allows them to operate effectively in infrastructure poor-areas. Unfortunately, the features that make them desirable also make them vulnerable to eavesdropping and modification attacks.

In order to address the security challenges of P2P networks, we propose an improvement of P2P paradigms devised in previous work [START_REF] Cassagnes | Overlay Addressing and Routing System Based on Hyperbolic Geometry[END_REF][START_REF] Tiendrebeogo | Reliable and scalable distributed hash tables harnessing hyperbolic coordinates[END_REF] and a new approach for key exchange that generalizes a model proposed by Takano et al. [START_REF] Takano | Multipath key exchange on p2p networks[END_REF]. On the one hand, we can still benefit from a truly scalable P2P overlay using hyperbolic coordinates where each node can decide on the strategy for accepting a parent node depending on its security priorities. On the other hand, we do not restrict our security scheme to a specific P2P topology, thus allowing our approach to be implemented in any P2P setup without any organization constraint.

In future work, we plan to implement a prototype of our system and assess it with simulation tools as well as with real-world experiments.

Fig. 1 .

 1 Fig. 1. A regular tree in the hyperbolic plane where each node has a degree 3 (Image credit: Cassagnes et al. [3])

  Saif et al. have reported that while a 2 Mbps ADSL link in the United States costs around US$40/month, a 2 Mbps broadband connection in Pakistan costs close to US$400/month

Algorithm 2 :

 2 Attempt a connection to a parent node p

			Algorithm 3: Paths selection
			Input: keyToTransmit, nodeNeighbours
	Input: p, alreadyConnectedParentAddrs	nodeNeighbours ← sortConnectedNodes(nodeNeighbours );
	allAddrLinkedToParent	keyComponents ← splitKey(keyToTransmit );
	Output: newAddr	usedN ode ← lastOf(nodeNeighbours );
	newAddr ← ⊥;	while keyComponents = ∅ do
	(proposedChildAddr,allAddrLinkedToParent) ← requestAddrToParent(p);	if usedN ode = lastOf(nodeNeighbours ) then usedN ode ← firstOf(nodeNeighbours )
	foreach addr ∈ alreadyConnectedParentAddrs do if ∃pa ∈ allAddrLinkedToParent | pa = addr	else	usedN ode ← nextOf(nodeNeighbours )
	then	return ⊥; /* try to connect to a	component ← firstOf(keyComponents );
		different parent */	transmitViaSeparatePath(component, usedN ode)
			;
	newAddr ← proposedChildAddr;	keyComponents ← keyComponents \ {component};
	allAddrLinkedToParent ← allAddrLinkedToParent ∪ {p};
	return newAddr;

see algorithm 1 and next section for more details on the dispatching technique


Imane Horiya Brahmi
email: horiya-imane.brahmi@ucd.ie

Soufiene Djahel
email: soufiene.djahel@ucd.ie

Damien Magoni
email: magoni@labri.fr

John Murphy
email: j.murphy@ucd.ie

An Efficient Partial Data Aggregation Scheme in WSNs

Keywords: Data aggregation, Dynamic waiting time, Periodic packet transmission, Spatial correlation, WSN

Highly accurate event detection makes Wireless Sensor Networks popular for real time monitoring. Wireless sensor systems that monitor physical and environmental conditions are expected to be deployed with high density, a situation which leads to spatial correlations and redundancy of collected data. Eliminating these redundancies extends the network lifetime by reducing energy consumption and enhances the velocity of transmitting emergency and periodic sampling. We consider in this paper the scenario where sensors are grouped into clusters. Each Cluster Head (CH) receives sampling from its Cluster Member (CMs), and decides when it should stop sampling and start the transmission of the aggregated packets in order to reduce the transmission delay while ensuring the accuracy of the transmitted data. In this work, we propose a cluster based aggregation scheme which determine the best timing at the CH level for achieving a short delay, and a buffer management strategy for maintaining low energy consumption. Evaluation results based on simulations show that our scheme achieve a good trade-off between energy consumption and end-to-end delay.

I. INTRODUCTION

Wireless Sensor Networks (WSNs) [START_REF] Akyildiz | Wireless sensor networks: a survey[END_REF] is an emerging technology for event detection and information gathering. Their main feature is to cooperatively sense specified events of interest in the sensor field, and transmit them toward the sink for processing via multi-hop routing protocol. This type of network has become popular due to its low cost and high accuracy detection. It has diverse applicability that includes areas such as environment, health, agriculture, and military, where different physical conditions are monitored such as temperature, humidity, vibration, air quality, etc.

Sensors are resources constrained with limited processing, storage and battery. A simple solution to this problem could be the replacement of the sensors' battery power. However, this solution is not convenient, mainly when the sensors are used for applications, such as monitoring hostile or non accessible areas like volcanos. In addition, sensor node density in the network may vary spatially and temporally depending on the application requirements [START_REF] Villas | A spatial correlation aware algorithm to perform efficient data collection in wireless sensor networks[END_REF]. With high density the reported data is likely to be redundant, correlated or copied. We usually associate the primary energy consumption with communication: it is well known that the communication is often the most expensive activity in terms of energy [START_REF] Pottie | Wireless integrated network sensors[END_REF], as the radio communication is the major source of energy consumption [START_REF] Somov | A methodology for power consumption evaluation of wireless sensor networks[END_REF].

An effective solution of reducing the communication overhead is data aggregation. In-network processing and data aggregation is defined as the process of gathering the data, preprocessing and computing it in the network itself and transmitting the extracted and required data to the sink. Hence, energy is conserved by eliminating redundancy and minimizing the transmission of raw data to the sink [START_REF] Fasolo | In-network aggregation techniques for wireless sensor networks: a survey[END_REF].

Delaying the transmission of the data packets, in order wait for more packets from neighboring nodes may increase the degree of aggregation [START_REF] Yousefi | Structure-free real-time data aggregation in wireless sensor networks[END_REF], [START_REF] Li | Energy-efficient and high-accuracy secure data aggregation in wireless sensor networks[END_REF]. However, in some applications such as monitoring, it is very critical to deliver the readings in a timely manner, especially in monitoring applications. For this purpose, the solutions designed for the WSNs should consider the end-to-end delay in addition to the energy consumption in their conception.

The new generation sensor nodes have been enhanced with significant energy-efficient storage, processing capabilities and data management abilities [START_REF] Wang | Dependable and secure sensor data storage with dynamic integrity assurance[END_REF]. Indeed, sensor nodes can be endowed with energy-efficient storage such as new-generation flash memory with several gigabytes of storage and lowpower consumption [START_REF] Mathur | Ultralow power data storage for sensor networks[END_REF], [START_REF] Mitra | High performance, low power sensor platforms featuring gigabyte scale storage[END_REF]. This emergence of low cost and high-capacity storage and processing prompts us to design a new cluster based aggregation scheme and buffer management strategy that satisfies the trade off between the energy consumption and end-to-end delay, by reducing the number of unnecessary redundant transmissions.

The remainder of this paper is organized as follows. In Section II, we present the related contributions on data aggregation in WSNs and highlight their limitations. Next, we present our scheme in detail in Section III. In Section IV, we present and discuss our results. Finally, we conclude the paper in Section V.

II. RELATED WORK

Two surveys [START_REF] Fasolo | In-network aggregation techniques for wireless sensor networks: a survey[END_REF], [START_REF] Rajagopalan | Data aggregation techniques in sensor networks: A survey[END_REF] have explored the most significant contributions dealing with data aggregation based on cluster approaches - [START_REF] Lindsey | Pegasis: Power-efficient gathering in sensor information systems[END_REF], [START_REF] Heinzelman | Energyefficient communication protocol for wireless microsensor networks[END_REF]. In this latter, a Low Energy Adaptive Clustering Hierarchy (LEACH) is proposed. LEACH protocol is based on two phases: a setup phase during which the network is organized into clusters and the cluster heads are chosen, and a steady phase in which the data is aggregated and transmitted to the sink. Another protocol extending LEACH protocol is presented in [START_REF] Younis | Heed: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks[END_REF], where the cluster head is selected periodically according to the node residual energy.

More recently, the authors of [START_REF] Villas | A spatial correlation aware algorithm to perform efficient data collection in wireless sensor networks[END_REF] studied the problem of constructing a spatial correlation by proposing dYnamic and scalablE tree Aware of Spatial correlaTion (YEAST) algorithm. The sensor nodes that detect the same event are grouped in a correlated region and the CH is selected and rotated in each round. However, this protocol is appropriate in event driven not for a periodic monitoring when the data is transmitted.

III. SYSTEM DESIGN

We consider in our scheme that sensor nodes are organized into clusters as shown in Figure 1 to make the aggregation and the communication easier and more efficient. Each CH gathers readings from its CMs, and aggregates them before sending the resulting packets toward the sink. We define the gathering time as the Local Waiting Time (W T Local ). This time is dynamic and determines when the CHs should aggregate the received readings and forward the resulting packets before the end of the monitoring period. These aggregated readings are then sent onward towards the sink via multi-hop routing path. We assume that these sensors are equipped with buffer memory and queue as shown in the Figure 2. The CHs' buffer is used for storing the received packets from their CMs. Each CH waits for a minimum number of samples before it aggregates and forwards the aggregated packets. A copy of the aggregated samples are then stored in the CH buffer. These copies of aggregated samples are used for aggregating future samples received from other sources (i.e. CMs). The forwarding queue is used for storing the aggregated packets which are now ready to be compared against future packets. The samples stored in the buffer have a limited storage time. Therefore, we define a Time out (T out ) for the packets stored in the buffer. This T out represents the maximum storage time for each packet in the buffer. Our focus in this work is to design a dynamic local waiting time and a T out mechanism for the packets in the buffer such that they optimize the energy usage, buffer storage

A. Waiting Time Computation

The CMs start periodically measuring given parameters, and forward the reading to their CH in the network. We assume that the CMs start collecting data at the same time (i.e. the beginning of each period). At the end of each monitoring period, the CMs start forwarding their readings (i.e. one reading per CM). The arrival time of the readings at the CH level is random. The aggregation process starts after receiving these first samples.

Our mechanism uses the following key parameters: a minimal number of samples received by the CH, depending on the application, the mean of the received samples values, the calculated standard deviation σ cal and we also define a standard deviation threshold σ th which depend on the application. These parameters are used to select appropriate time to start aggregating the received packets and forward the resulting packets.

When the first samples are received by the CHs, they start the aggregation process, compute the mean and standard deviation (σ cal ) of the received values, and compare them with σ th . If the minimal number of required samples is reached, and the threshold condition is satisfied, the samples can be aggregated and sent onward. If a received sample reports a value exceeding a given upper bound of the measured parameter (e.g. a vehicle out of control on the road), this sample is forwarded immediately as an emergency message.

Once the conditions of minimal number of required samples and σ cal is met, the aggregated packets are forwarded and a copy of these aggregated packets is stored in the CH's buffer for limited period of time T out .

B. Sensor Buffer Management Strategy

Since the buffer capacity and energy consumption are limited in WSN nodes, our second objective is to optimize the energy consumption of sensor nodes (CHs) by keeping the packets in the buffer an optimal time period. In this section we define a scheme for sensor buffer management and processing of the packets, and illustrate the T out calculation, which ensures an optimal trade-off between energy consumption and packet storage duration. Our T out calculation is a function of the following parameters: the sensors nodes popularity, the frequency of receiving the same packets from the same category, their priority level, the residual energy of each CH and the buffer size of the CHs.

T out = f (R, F, W, E) (1)
Where:

• R: popularity of the CH: computed based on the traffic load transiting trough this CH

• F: frequency of receiving the same packets from the same category

• W: packet's priority level • E: residual energy of the CH node (E res , E Max ) We assume that the maximum energy level and the buffer capacity of each node are both known in advance. The popularity of a node is defined by the ratio R = N bof P acketsT ransmitted N bof P acketsReceived . The closer the nodes are to the sink, the longer they retain their packets in the buffer, since the traffic density is higher in the surrounding of the sink.

We assume that:

T out = [0, T outMax ] (2)
The initial T outMax0 depends on the energy and the buffer capacity of the sensor nodes. At t = 0 (when the network starts running) the buffer is empty and the energy capacity of the node is at its maximum level, thus we have T outMax0 = T outMax . In order to give more priority to the packets with higher priority level, every time a packet (P n ) is received by the CHs, they check the category of the packet. For each type (i.e., based on the parameter measured: vehicle speed, temperature, etc.) and category, we adjust the T outMax according to the priority level of the packet received by defining a weighting mechanism. The T outMax0 is proportional to the level of priority of the packet. If a packet has a high priority level, then the probability to retain the packet longer in the buffer is higher.

T outMax0 (P er i ) = T outMax0 × W (P er i ) (3)
Where: W (P er i ) = P riority i P riority max (4) Such that (P er i ) refers to the type of a periodic packet and W (P er i ) is its corresponding weight. P riority i indicates the priority level of a given packet type. Once the buffer is full and a new packet arrives, the sensor executes Algorithm 1. When new packets are received, the CHs have to decide which packets are the most essential for future aggregation. For this, the CHs check the frequency of receiving the same packets from the same category, which will indicate any unusual event. The packet with the smallest T out and frequency and the lowest priority will be deleted first. Next, each CH calculates the new T out based on its remaining battery power. If the same packet is received many times, its T out will be calculated according to its corresponding Average Inter-Arrival time (T AIA ), for the purpose of extending its storage time for future aggregation.

Algorithm 1 Sensor Buffer Management Algorithm 1: At t = T (when the buffer is full) 2: if new packet received then if T out ≤ T AIA then 7:

T out (P n ) = T outMax0 (P type )[( Eres(node) EMax(node) )×R]+ T AIA 8: else 9: T out (P n ) = T outMax0 (P type )[( Eres(node) EMax(node) ) × R] 10:
end if

11: else 12:
Add the packet to the buffer and calculate its T out 13:

end if 14: end if In this section, we evaluate and discuss the performance of our proposed scheme (Partial Aggregation: P Agg ) using the ns-2.35 [START_REF]The network simulator ns-2[END_REF] network simulator. We have compared the P Agg to the Full aggregation (F Agg ) where the CHs wait to receive packets from all their CMs, and Aggregation Off (Agg Of f ) where the received packets are forward immediately upon their reception. Our solution is static cluster based and the cost of deployment and maintenance of the clusters is not considered. The generation of the packets by the CMs and their values are random during each periodic monitoring interval. Once the first packets are received by the CHs, the aggregation process starts by computing the standard deviation (σ cal ) of the received packets value and comparing it with σ th , in order to decide when the aggregated packets should be sent. Once the condition is met the CH aggregates the received packets, forwards them and stores a copy in their buffer.

IV. PERFORMANCE EVALUATION

We summarize in Table I the default parameters used in the simulation. The primary metrics evaluated are: (i) the Average End-to-End (E2E) transmission delay of all the packets, which represents the average time needed for a packet sent by the source to cross the network and reach the sink. (ii) The Energy Consumption (EC) defined by the ratio of the total energy used when the aggregation is enabled to the total energy used when the aggregation is disabled.

The results plotted in Figure 3 compare the average end-toend delay of the three schemes. In this scenario we have set the network size to 100 nodes and varied the monitoring period. From these results, we can see that the Agg of f achieves negligible E2E delay, this is explained by the immediate forwarding of the received packets from the CMs. The early aggregation in the P Agg leads to a lower E2E delay compared to the F Agg , this is due to the long waiting time scheme used in F Agg . We notice that the difference achieved is equal to 20% on average, and increases to approximately 60% with the increase of the monitoring period. From this we can say that the long waiting time is unnecessary in F Agg .

In Figure 4 we notice that the energy consumption achieved by P Agg is as low as that achieved by F Agg . Also the energy consumption increases with the monitoring period interval. The main objective of our work is to achieve a lower endto-end delay of the packet transmission, while keeping a low energy consumption in the network. We can conclude from our results that our scheme succeeds in this.

V. CONCLUSION

We have proposed in this work a new cluster based aggregation scheme for periodic monitoring to deal with the problem of long waiting times in WSNs at the CH level. We have first defined a solution based on a dynamic waiting time which uses an optimal timing for aggregating and forwarding the packets. Next, we proposed a buffer management strategy for processing the stored packets in an efficient way such that the energy power is saved and the optimally aggregated packets are accurate. The performance evaluation results have proven the efficiency of our scheme in terms of end-to-end delay and energy consumption.

Fig. 1 .

 1 Fig. 1. Clustered WSN topology.

Fig. 2 .

 2 Fig. 2. Representation of a sensor's buffer.

3 :if packet exists then 5 :

 35 Check if the same packet already exists in the buffer 4:Check T AIA , the packet's category and type 6:

Fig. 3 .

 3 Fig. 3. Average end-to-end delay vs monitoring period (Network Size = 100 nodes).

Fig. 4 .

 4 Fig. 4. Energy consumption vs monitoring period (Network Size = 100 nodes).