Routing in Wireless Sensor Networks a Comparative ...

International Conference on Embedded Systems in Telecommunications and Instrumentation (ICESTI'14), Annaba, Algeria, October, 27-29, 2014

Routing in Wireless Sensor Networks a Comparative Study: Between AODV and DSDV Dahane Amine1, Berrached Nassreddine2, Hibi Abdennacer3, Loukil Abdelhamid4, Kechar Bouabdellah5 1,2,4 Intelligent Systems Research Laboratory University of sciences and technology, Oran, Algeria 5 Laboratory of Industrial Computing and Networking (RIIR), Oran University, Oran, Algeria 3 Computer Science Department, Bechar University, Algeria [email protected] Abstract— A wireless sensor network (WSNs) is constituted of several sensors deployed to monitor an activity or a certain area. These sensors have several functions including acquisition, processing, storage, and data. However, they include limited energy resources (batteries) due mainly to their small size. Moreover, it is difficult or even impossible to replace the batteries of the sensors. Thus, routing protocols must consider the energy constraint to minimize energy consumption and therefore maximize the life of the global network. So, our objective in this work is to show clearly the interest of the routing protocols in Energy saving, using a comparative study between two well known routing protocols AODV and DSDV.

this comparison we just focus on the energy consumption. We have chose those two protocols basing on their similarity for example they both based on distance vector routing which mean that they select the best routing path based on a distance metric then comes the vector which is the interface traffic will be forwarded out in order to reach an given destination. Furthermore, their large use and study in the researches. And, finally because they are among the first routing protocols adopted for the sensors networks.

Keywords—Wireless Sensor Networks, AODV, DSDV, Energy Efficiency.

I. INTRODUCTION Wireless Sensor Networks (WSNs) have grown to become one of the most promising and interesting fields over the past few years. WSNs are wireless networks consisting of distributed sensor nodes that cooperatively monitor physical or environmental conditions. A sensor node is a tiny and simple device with limited computational resources. Sensor nodes are randomly and densely deployed in a sensed environment. WSNs are designed to detect events or phenomena, and collect and return sensed data to the user. WSNs have been used in many applications such as battlefield surveillance, traffic monitoring, healthcare, environment monitoring, etc. Communication in WSNs is subject to various phenomena that characterize the communication by radio wave. The most known is the signal attenuation with distance, which prevents the communication between two nodes that are without direct communication with each other and forces the packets relay via intermediate nodes. The same can be said for message corruption when two close nodes emit simultaneously (collision), and problems like hidden and exposed nodes which are phenomena proper to WSNs. Therefore, the routing protocol must be efficient to overcome these problems. Recently, routing protocols designed for WSNs have been widely studied. The methods can be classified according to several criteria which are illustrated in Fig. 1. From the routing protocols studied in the [1,8], we chose two routing protocols from two different categories which are Ad hoc On Demand Distance Vector (AODV) for reactive approach routing and from the proactive approach we have chose Destination Sequenced Distance Vector (DSDV) [2,12], In

Fig.1. WSNs Routing Protocols Classification The rest of the paper is organized as follows. Section II presents a conceptual view, using UML methodology, of simulator mercury. Section III introduces and explains the main assumptions and definitions that we adopted in our work. In section IV we present a description for the AODV and DSDV protocols used for the simulation. Simulation results are provided to show the effectiveness of the reactive routing protocol (AODV) and it has a significant impact on the network lifetime in Section V. Section VI concludes the paper and outline directions of future work. 1


II. MODELING USING UML In this section we first present the design of Mercury simulator [3] using UML model, and then provide some details of its implementation under C Borland language in section 5. A. Identification of Actors - Network Administrator: An administrator can deploy the nodes, add and delete nodes, give the order to the Sink to activate the nodes. - Sink: the Sink can activate the nodes, communicates with administrator, and form the routing table. - Sensor: the sensor may detect, send and receive data. B. The class diagram It’s one of the most important diagrams in objectoriented development. On the functional side, this graph is intended to develop the structure of the entities manipulated by users. At designing side, the class diagram shows the structure of object-oriented code (Fig.2).

Fig.3. The network administrator sequence diagram

Fig.2. The class diagram. C. The sequence diagram It represents the exchange of messages between objects, in the context of a particular operation of the system. In Fig.3, the system sends the following messages: “Deployment performed”, “Sensors activated”, “Routing table formed”, “data received”. However, the system receives the following messages: “Effectuate the Deployment”, “Form the Routing Table”. Fig.4 shows the sequence diagram of the Sink. The system sends the following messages:“Sensors activated”, “Communication effectuated”, “routing table formed”, “Data received” however the system receives the messages: “Communicate”, “Form the routing table”, “Send Data”. The roles taking part in these sequences are: Administrator, Sensor, System, and Sink. Fig.5 shows the sequence diagram of the Sensor. The system sends the following messages: “Sensors activated”, “Data received”, “Routing table formed” however the system receives the following messages: “Send Data”, “Send the routing data”.

Fig.4. The sink sequence diagram

Fig.5. The sensor sequence diagram 2


III. PRELIMINARIES Assumptions and Definitions In this section, the main assumptions and definitions that we adopted in our work are presented. All sensor nodes are deployed randomly in a plane of two dimensions 2D. The radio coverage of a sensor node is a circular region centered at this node with radius R. Two sensor nodes cannot be deployed in exactly the same position (x, y) in 2D space. All sensor nodes are identical or homogeneous. For example, they have the same radio coverage radius R. Each node can determine its position at any moment in 2D space. The Sink is a node having sufficient capacity in terms of computation, communication, and memory of storage and energy autonomy. In our work, we do not take the target into consideration. Definition 1: The radio coverage of the network is the portion of the area covered by sensor nodes. Definition 2: Euclidean distance between two points u and v in a 2D space is denoted d (u, v) with: d (u, v)= ( 1 − 2) + ( 1 − 2, where (x1, x2) and (y1, y2) denote respectively the coordinates of the nodes u and v in a 2D space. Definition 3: NS (u): set of neighboring nodes of node u, is formally defined by: NS (u) = {| (,) ≤}, where N is the nodes number. 1) The distance between node and its neighbors (Di) This is likely to reduce node detachments and enhance cluster stability. For each node i, we compute the sum of the distance with all its neighbors j. This distance is given, as in [3, 9], by: = (, ) (1) ∈ ()

2) The residual energy of a node ( ) After transmitting a message of k bits at distance from the receiver, this energy is calculated according to [3, 10]: = − (, ) + ! "#"$ ()% (2)

Where: - : The node’s current energy; - (, ) = . "#"$ + . ()* . + : refers to the energy required to transmit a message; ()* is the required amplifier energy. - ! "#"$ () = "#"$ : refers to the energy consumed while receiving a message. 1) AODV Being a reactive routing protocol AODV uses traditional routing tables, one entry per destination and sequence numbers are used to determine whether routing information is up-to-date and to prevent routing loops. AODV (ad hoc on-demand distance vector) [2, 12, 13] is a distance vector routing protocol that operates reactively to reduce overhead finding routes only on demand. When a route does not exist to a given destination, a route request (RREQ) message is flooded by the source and by the intermediate nodes if they have no previous routes in their table. Upon receiving a RREQ message, the receiving

node will record the route information in its own routing table. Once the RREQ message reaches the destination or an intermediate node, the node responds by unicasting a route reply (RREP) message back to the neighbor from which it first received the RREQ message. As the RREP message is forwarded back along the reverse path, nodes along this path set up forwarding entries in their routing tables, pointing to the node from which they received RREP message. Fig. 6 will resume the whole process from route discovery until the route maintenance. 2) DSDV The DSDV (destination-sequenced distance vector) protocol [2, 12] uses the Bellman-Ford algorithm to calculate paths. The cost metric used is the hop count, which is the number of hops it takes for the packet to reach its destination. DSDV is a table-driven protocol. Every node maintains a routing table that lists all available destinations, the number of hops to reach the destination and the sequence number assigned by the destination node. The sequence number is used to distinguish stale routes from new ones and thus avoid the formation of loops. The nodes periodically transmit their routing tables to their immediate neighbors. When a route update with a higher sequence number is received, it replaces the old route. In case of different routes with the same sequence number, the route with the better metric is used. Updates have to be transmitted periodically or immediately when any significant topology change is detected. IV. COMPARISON OF AODV AND DSDV AODV Criteria Advantages of AODV Because of its reactive nature, AODV can handle highly dynamic behavior networks. Unicast, Broadcast and Multicast communication. On-demand route establishment with small delay. Link breakages in active routes efficiently repaired. All routes are loop-free through use of sequence numbers. AODV reacts relatively quickly to the topological changes in the network and updating only the hosts that may be affected by the change. Limitations of AODV No reuse of routing info: AODV lacks an efficient route maintenance technique. The routing info is always obtained on demand, including for common cause traffic. AODV does not discover a route until a flow is initiated. This route discovery latency result can be high in large scale mesh networks. AODV lacks support for high throughput routing metrics: AODV is designed to support the shortest hop count metric. This metric favors long, low bandwidth links over short, high bandwidth links. No reuse of routing info: AODV lacks an efficient route maintenance technique. The routing info is always obtained on demand, including for common case traffic. 3


a D S c b

(3)

c & D establish reverse route c broadcasts an RREQ D unicast RREP

a D S c b

(5)

S establishes route

Fig. 6. The whole process of AODV from route discovery until the route maintenance. 4


DSDV Criteria

Advantages of DSDV DSDV protocol guarantees loop free paths. We can avoid extra traffic with incremental updates instead of full dump updates. Path Selection: DSDV maintains only the best path instead of maintaining multiple paths to every destination. With this, the amount of space in routing table is reduced.

B. Discussion and results This section provides our experimental results and discussions

Limitations of DSDV Wastage of bandwidth due to unnecessary advertising of routing information even if there is no change in the network topology. DSDV doesn’t supports Multi path Routing. It is difficult to determine a time delay for the advertisement of routes. It is difficult to maintain the routing table’s advertisement for larger network. Routing in sensor networks has attracted a lot of attention in the recent years [8], Table.1 shows the difference between reactive and proactive approach. V. IMPLEMENTATION AND RESULTS In this section, we present our simulator where it’s implemented using the C Borland language A. The simulator mercury There are some simulators developed or adapted for WSNs, such as SensorSim [4], TOSSIM [5], and Power-TOSSIM [6]. Unfortunately, none of them are appropriate for our purpose. We attempt to complement the theoretical study by implementing our own wireless sensor network simulator Mercury [3] (see Fig. 7). It is based on an object-oriented design and designed with three goals in mind: performance, modularity, and extensibility. We have implemented a discrete event model, in which the analysis objects (base station, nodes).

Fig. 22 The initial configuration of nodes

Reactive Routing Protocol

Proactive Protocol

Routing

Reaction to the demand by diffusing requests

Control packets exchange

No routing table maintained continuously Significant cost for the roads’ development

Continuous updating of routing tables

The roads are immediately available on demand

Considerable delay before sending a packet

The delay before sending a packet is minimal

No continuous traffic for unused routes

Control and update traffic may be important and partially useless

Reduced use of the bandwidth

Large portion of the bandwidth to keep routing information up-to-date

Less energy consumption.

More energy consumption for large networks.

Table 1. Comparison of proactive and reactive routing protocols

Fig. 7. The simulator mercury

The result of the remaining amount of energy per node for each protocol AODV and DSDV is depicted in Fig.21 such as R equal to 35 m. As shown in the figure, the remaining energy for each node in AODV protocol is greater than that in DSDV protocol such as AODV consumes 22, 74% less than DSDV. According to the result, the network consumes 19, 23 % of total energy when we use AODV protocol

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(192 322,091 NJ) however it consumes 41, 97 % with DSDV protocol (419 740, 129 NJ). We observe also the network lost 6 nodes with DSDV but only one node with AODV because of depletion of its battery. This result proves affirmatively that AODV outperforms DSDV. This is due to the tremendous overhead incurred by DSDV when exchanging routing tables and the periodic exchange of the routing control packets. So our algorithm gave better performance in terms of saving energy when it is coupled with AODV.

Fig. 23 Snapshot showing the neighborhood of the sink exhaust their energy (N=60, R=30m)

VI. CONCLUSIONS AND FUTURE WORKS

Fig.21 Remaining energy per node using ES-WCA

In Fig. 22, we evaluate the network lifetime by varying the number of nodes such as R equal to 70m. We consider that the network will be invalid when the nodes of the neighborhood of the sink exhaust their energy as illustrated by Fig.23. There are 9 nodes in an active state but the network is invalid. We note that the increase in the number of nodes does not have a significant impact on the network lifetime except between N=60 and N=80. When there were 20 nodes in the network, AODV increases the network duration about 88, 47 % than DSDV and about 57,9% for N=100. Also, this is due to the fact that in DSDV protocol each node must have a global view of the network, which consequently increases the number of the exchanged control packets (overhead) in the whole network, and decreases the remaining energy of each node, which has a direct affect on the network lifetime.

In this paper, Protocol AODV is comparable with DSDV to show clearly the interest of the routing protocols in Energy saving. However, the AODV becomes very important in case of a high node density. This is due to the tremendous overheads DSDV when exchanging routing tables and exchanging routing control packets. As a result of this work, we plan to exploit the concept of redundancy to enhance results that are related to energy conservation. Another interesting work that remains to do is to provide in-network processing by aggregating correlated data in the routing protocol. This considerably reduces the amount of data that are transported in the network. In addition, the research community has developed and refined several techniques for energy conservation [7], so hybridizing several techniques in the same protocol will give remarkable results.

ACKNOWLEDGEMENTS The authors are grateful to the anonymous referees and the guest editors for valuable suggestions which improve the quality of the paper.

REFERENCES [1]

[2] [3]

[4]

[5]

Fig. 22 Network lifetime depending on number of nodes using ES-WCA

V. Geetha, P.V. Kallapur, S. Tellajeera, "Clustering in Wireless Sensor Networks: Performance Comparison of LEACH & LEACH-C protocols Using NS2", Procedia Technolgy (C3IT-2012), Elsevier, Vol.4, pp.163-170, 2012. C.E. Perkins and P. Bhagwat, “Highly dynamic destination-sequenced distance vector routing (DSDV) for mobile computers,” in Proc. ACM SIGCOMM 94, London, UK, pp. 234-244, Oct. 1994. A. Dahane, N. Berrached , B. Kechar , “Energy Efficient and Safe Weighted Clustering Algorithm for Mobile Wireless Sensor Networks”, The 9th International Conference on Future Networks and Communications (FNC 2014), Procedia Computer Science (Elsevier) , Vol.34, pp.63-70, 2014. S. Park, A. Savvides, and M. B. Srivastava, Sensorsim: A simulation framework for sensor networks, in Proc of the 3rd ACM Int’l Workshop on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pp. 104–111, 2000. P. Levis, N. Lee, M. Welsh, and D. Culler, Tossim: Accurate and scalable simulation of entire tinyos applications, in Proc of the 1st Int’l Conf on Embedded Networked Sensor Systems, pp. 126–137, 2003.

6


[6]

[7] [8] [9] [10] [11]

[12]

[13]

V. Shnayder, M. Hempstead, B. rong Chen, G. W. Allen, and M. Welsh, Simulating the power consumption of large-scale sensor network applications, in Proc of the 2nd Int’l Conf on Embedded Networked Sensor Systems, pp. 188–200, 2004. B. Kechar, “Problématique de la consommation de l’énergie dans les réseaux de capteurs sans fil”, Phd thesis, Oran University, July 2010. K. Akkaya, M. Younis, “A survey on routing protocols for wireless sensor networks”, in the Elsevier Ad-Hoc Network Journal, , 2005. Zabian, A. Ibrahim, F. Al-Kalani, “Dynamic Head Cluster Election Algorithm for clustered Ad-hoc Networks”, Journal of Computer Science, Vol.4, No.1, 2008. S. Soro and W.B. Heinzelman, “Cluster head election techniques for coverage preservation in wireless sensor networks”, Ad-Hoc Networks Journal (Elsevier), Vol.7, No.5, 2009. W. Choi, M. Woo, “A Distributed Weighted Clustering Algorithm for Mobile Ad Hoc Networks”, Proc. of the Advanced International Conference on Conference on Telecommunications and International Conference on Internet and Web Applications and Services (AICT/ICIW 2006), IEEE, 2006. N. Hemanth, C. Yufei, C. Egemen K, R. Justin P. and S. James P.G., “Destination-Sequenced Distance Vector (DSDV) “Routing Protocol Implementation in ns-3”, WSN-3, Barcelona, Spain, pp. 439-446, March 21. 2011. G. Rob van, T. Wee Lum, H. Peter, P. Marius “Sequence Numbers Do Not Guarantee Loop Freedom —AODV Can Yield Routing Loops— “, MSWiM’13, November 3–8, 2013, Barcelona, Spain, pp. 91-100, 2013.

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