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Performance Evaluation of Mobility speed over Directed Diffusion Routing Protocol K.E.Kannammal and Dr.T.Purusothaman Abstract— Recent technological advances have enabled distributed information gathering from a given region by deploying a large number of networked tiny micro sensors which are low power devices equipped with programmable computing, multiple sensing and communication capability thus forms Wireless Sensor networks. Directed Diffusion is a data centric routing protocol in Wireless Sensor Networks (WSN). It is a reactive protocol which creates routes based on needs. Sensed data’s are stored in attribute value pairs. The Sink node request data by sending interests. The interest messages are flooding through the network and are added to each node’s interest cache. The data that match the interests are sent towards the sink. However, there is very little research addressing the impact of mobility on this class of routing protocols.In this paper, we evaluated the performance of Directed Diffusion under different scenarios and mobility. Index Terms— Directed diffusion, Drain rate, Power Consumption, Wireless Sensor Networks, mobility.

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1 INTRODUCTION Wireless sensor networks are information gathering paradigm based on the collective efforts of many small wireless sensor nodes. The sensor nodes, which are intended to be physically small and inexpensive, are equipped with one or more sensors, a short-range radio transceiver, a small micro-controller, and a power supply in the form of a battery or mechanisms that try to scavenge energy from the environment. Sensor network deployments are envisioned to be done in large scales, where each network consists of hundreds or even thousands of sensor nodes [1,12]. In such deployment, human configuration of each sensor node is usually not feasible and therefore selfconfiguration of the sensor nodes is important. Energy efficiency is also critical, especially in situations where it is not possible to replace sensor node batteries. In commercial sensor network installations, e.g., intelligent buildings, battery replacement is typically no option as the systems should allow maintenance-free operation. Most sensor network applications aim at monitoring or detection of phenomena. Examples include office building environment control, wildlife habitat monitoring, and forest fire detection. For such applications, the sensor networks cannot operate in complete isolation; there must be a way for a monitoring entity to gain access to the data produced by the sensor network. By connecting the sensor network to an existing network infrastructure such as the global Internet, a local-area network, or a private intranet, remote access to the sensor network can be achieved. The sensor network architecture is shown in ————————————————

Associate Profeesor, Department of Computer Science and Enginnering Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamilnadu, India. Assistant Professor , Department of Computer Science and Enginnering Government College of Technology, Coimbatore, Tamilnadu, India.

figure 1.Traditional routing protocols defined for wireless ad-hoc networks are not well suited for wireless sensor networks due to the following reasons [2]:

Figure 1. The sensor network

Sensor networks are data centric. Traditional networks usually request data from a specific node, but sensor networks request data based on certain attributes such as "which area has temperature > 100°F?'' In traditional wired and wireless networks, each node is given a unique id, which is used for routing. This cannot be effectively used in sensor networks because being data centric they do not require routing to and from specific nodes. Adjacent nodes may have similar data. So instead of sending data separately from each node to the requesting node, it is desirable to aggregate similar data before sending it. The requirements of the network change with the application and hence, it is application-specific.

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Thus, sensor networks need protocols which are application specific, data-centric, capable of aggregating data and minimizing energy consumption [3]. The organization of the paper is as follows. Section 2 discusses the mobile sensor networks. Section 3 discusses features of the Directed diffusion protocol. Section 4 presents the mobility models used in this analysis. The simulation results are presented in section 5. Based on the analysis, section 6 presents the conclusions.

2. MOBILE SENSOR NETWORKS Mobile sensor networks are sensor networks in which nodes can move under their own control or under the control of the environment [4]. Mobile networked systems combine the most advanced concepts in perception, communication, and control to create computational systems capable of interacting in meaningful ways with the physical environment, thus extending the individual capabilities of each network component and network user to encompass a much wider area and range of data. A key difference between a mobile sensor network and a static sensor network is how information is distributed over the network. Under static nodes, a new task or data can be flooded across the network in a very predictable way. Under mobility this kind of flooding is more complex. Under natural mobility this depends on the mobility model of the nodes in the system

the path to update the timestamp and duration in the gradients. Directed diffusion is significantly different from IP-style communication[5] where nodes are identified by their end points, and inter-node communication is layered on an end-to-end delivery service provided within the network .

3.1. Interest Propagation An interest is usually injected into the network at some node in the network. The term sink generally used to denote this node.Flooding will be used for interest propagation. When a node receives an interest, it checks to see if the interest exists in the cache. If no matching entry exists, the node creates an interest entry. The parameters of the interest entry are instantiated from the received interest. This entry has a single gradient towards the neighbor from which the interest was received, with the specified event data rate. If there exists an interest entry, but no gradient for the sender of the interest, the node adds a gradient with the specified value. It also updates the entries timestamp and duration fields appropriately. Finally, if there exists both an entry and a gradient, the node simply updates the timestamp and duration fields. The interest propagation is shown in figure 2.

3. DIRECTED DIFFUSION Directed Diffusion is a data-centric routing algorithm for sensor networks. Its key features are named attributevalue pairs and path reinforcement [5]. Directed diffusion is a reactive routing protocol which creates paths based on need, not ahead of time. Sensed data is stored in attribute-value pairs. When a node known as a sink node wants information about a particular attribute, it broadcasts interest messages to its neighbors. These interest messages are flooding through the network and are added to each node's interest cache. Each interest record in this cache has one or more gradients which correspond to neighbor nodes that transmitted the interest. The gradient also stores the rate at which data is desired, the duration of the interest, and a timestamp. When a node generates data that matches an interest in its cache, it sends the data back to the source along the gradients. Intuitively, the data is drawn to the sink through the gradients. The sink node may reinforce the shortest path (i.e., the one with the fastest response) by sending an interest with a higher data rate along that path. Intermediate nodes propagate the reinforcement by examining a local cache of recently sent data messages. The data cache also prevents loops in data delivery. Slower data paths may be sent negative reinforcement, i.e. interest messages with a slow data rate to save network bandwidth. If a sink wants to continue receiving data it must periodically reinforce

Figure 2. The Interest Propagation Figure 3 shows the gradients established in the case where interests are flooded through a sensor field. Notice that every pair of neighboring nodes establishes a gradient towards each other. This is a crucial consequence of local interactions. When a node receives an interest from its neighbor, it has no way of knowing whether that interest was in response to one it sent out earlier, or is an identical interest from another sink on the "other side" of that neighbor. Such two-way gradients can cause a node to receive one copy of low data rate events from each of its neighbors. In sensor network, a gradient specifies both a data rate and a direction in which to send events. More generally, a gradient specifies a value and a direction [7,8]. The directed diffusion paradigm gives the designer the freedom to attach different semantics to gradient values.

3.2. Data Propagation A sensor node that detects a target searches its in-

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terest cache for a matching interest entry. In this case, a matching entry is one whose rect encompasses the sensor location, and the type of the entry matches the detected target type. When it finds one, it computes the highest requested event rate among all its outgoing gradients. The node tasks its sensor subsystem to generate event samples at this highest data rate. The source then sends to each neighbor for whom it has a gradient. This data message is, in effect, unicast individually to the relevant neighbors. A node that receives a data message from its neighbors attempts to find a matching interest entry in its cache. The matching rule is as described in the previous paragraph. If no match exists, the data message is silently dropped. If a match exists, the node checks the data cache associated with the matching interest entry[6]. This cache keeps track of recently seen data items. It has several potential uses, one of which is loop prevention. By examining its data cache, a node can determine the data rate of received events. To re-send a received data message, a node needs to examine the matching interest entries gradient list. If all gradients have a data rate that is greater than or equal to the rate of incoming events, the node may simply send the received data message to the appropriate neighbors.

Figure 3. The Data Propagation

3.3. Reinforcement Once sources detect a matching target, they send lowrate events, possibly along multiple paths, towards the sink. After the sink starts receiving these low data rate events, it reinforces one particular neighbor in order to "draw down" higher quality events. To reinforce this neighbor, the sink re-sends the original interest message but with a smaller interval .When the neighboring node receives this interest, it notices that it already has a gradient towards this neighbor. Furthermore, it notices that the sender‖s interest specifies a higher data rate than before. If this new data rate is also higher than that of any existing gradient, the node must also reinforce at least one neighbor.

Figure 4.Data Reinforcement

The node uses its data cache for this purpose. For example, this node might choose that neighbor from whom it first received the latest event matching the interest. Alternatively, it might choose all neighbors from which new events were recently received Through this sequence of local interactions, a path is established from source to sink transmission for high data rate events as shown in figure 4. The Reinforcement process Reinforce one of the neighbor after receiving initial data.

4. MOBILTY MODEL In the performance evaluation of a protocol for a wireless sensor network, the protocol should be tested under realistic conditions including, a sensible transmission range, and limited buffer space for the storage of messages, representative data traffic models, and realistic movements of the mobile users. The random way point model is the most commonly used mobility model in research community. In the current Network Simulator (NS-2) distribution, the implementation of this mobility model is as follows: at every instant, a node randomly chooses a destination and moves towards it with a velocity chosen randomly from [0, Vmax], where V max is the maximum allowable velocity for every mobile node. After reaching the destination, the node stops for a duration defined by the ―pause time‖ parameter. After this duration, it again chooses a random destination and most cases repeat the whole process again until the simulation ends. The random way point model is provides by the setdest tool in the standard NS-2 distribution

5. EVALUATION We have used the directed diffusion code in NS-2 implemented by USC/ISI [8] and mobility extensions that were implemented by the CMU Monarch project. For our simulations, we used a sensor network comprising of 30 nodes which are randomly dispersed on 800m x 800m field. Movement patterns were generated based on random way point model [9] for each value of pause time 2 and speed 2 to 12. The initial energy of all nodes is 40 Joules. For our experiments, we use a load of one sink/source and five source/sink nodes. The parameters chosen for simulation in NS2 in shown in the table 1. NS2 it is an open source discrete event simulator and it was developed at the University of California at Berkeley and the Virtual Inter Network Testbed [10,11]. Parameter Simulation Time No of nodes Pause time Environmental size Speed Mobility Model

Value 100 sec 30 2 sec 800 x 800m 2,4,6,8,10,12 m/s Random Way Point

JOURNAL OF COMPUTER SCIENCE AND ENGINEERING, VOLUME 2, ISSUE 1, JULY 2010 41

Table 1: parameters for simulation

Dropped Bytes: As shown in Figure 4 and 5 shows the Dropped bytes of the mobile nodes with varying number of source/sink nodes. It has been observed that number of dropped bytes is increases as number of sink nodes increases. Dropped Packets: As shown in Figure 6 and 7 shows the Dropped packets of the mobile nodes with varying number of source/sink nodes. It has been observed that number of dropped packets also increases as the number of sink nodes increases. Power Consumption: Figures 8 and 9 compares one source with varying sink nodes and one sink with varying sources with respect to speed and power consumption respectively. We observe that in both the scenarios the consumed power of the node is almost same.

Sensor Networks: A Survey,” IEEE Wireless Communication, Vol: 11, Dec. 2004, pp. 6-28. [8] Philip Sitton and Kenan Casey, "Directed Diffusion Report", COMP6360 [9] "Ns-2 network simulator," http://www.isi.edu/nsnam/ns/, 1998. [10] "Cmu monarch extensions to ns-2," http://www.monarch.cs.cmu.edu/cmu-ns.html,1999. [11] Marc Greis' Tutorial for the UCB/LBNL/VINT Network Simulator " ns" [12].. S. john, “Wireless Sensor Networks,” Department Of Computer Science, University Of Virginia, June 19, 2006

K.E.Kannammal has received her M.E in Computer Science from Sri Krishna College of Engineering and is working towards Ph.D. in Computer Science from the Anna University, Coimbatore. Currently she is working as a HOD & Associate Professor in CSE Dept, Sri Shakthi College of Engineering and Technology, Coimbatore,Tamil Nadu, India and she is having 18 years of Experience in Teaching.She had published papers in Various National Conferences and International Conferences held at many Engineering Colleges. Her interests include Network Security and Routing in sensor Networks.

6. CONCLUSION In this paper, we analyzed the impact of mobility and performance of Directed diffusion with increasing number of sources and sinks. Our simulation result shows that the node mobility influences the performance of the routing protocol. The various studies on routing protocol for mobility concluded to the following .The number of sources and sinks with mobility in a directed diffusion scenario affects the power consumption, Number of dropped packets as well as the Number of dropped bytes. .

REFERENCES [1]

[2]

[3]

[4]

[5]

[6]

[7]

I.F.Akyildiz, W.Su,Y.Sankarasubramaniam and E.Cayirci, “A Survey on Sensor Networks”, IEEE Communication Magazine , Volume: 40 Issue: 8, pp.102-114, August 2002W.-K. Chen, Linear Networks and Systems. Belmont, Calif.: Wadsworth, pp. 123-135, 1993. (Book style) Manjeshwar, A.; Agrawal, D.P."TEEN: a routing protocol for enhanced efficiency in wireless sensor networks", Parallel and Distributed Processing symposium., Proceedings 15th International Volume, Issue , Apr 2001 Page(s):2009 – 2015 Wendi Rabiner Heinzelman, Anantha Chandrakasan and Hari Balakrishnan “ Energy –Efficient Communication protocol for wireless sensor Networks” Proc. 33rd Hawaii Int'l. Conf. Sys. Sci., Jan. 2000. Ankur Choksi, Richard P. Martin, Badri Nath and Rahul Pupala, “Mobility Support for Diffusion-based Ad-Hoc Sensor Networks”, Department of Computer Science, Rutgers University, Piscataway, NJ, Technical Report DCS, April 2002 C. Intanagonwiwat, R. Govindan, and D. Estrin, “Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks,” in Proceedings of the ACM/IEEE International Conference on MobileComputing and Networking (Mobicom ’00), Boston, USA, 2000. . P.Lambrou and C. G. Pamayiotou, “Collaborative Area Monitoring Using Wireless Sensor Networks with Stationary and Mobile Nodes,” EURASIP Journal on Advances in Signal Processing ,Volume 2009, Mar 2009. J. N. Al-Karaki and A. E. Kamal, “Routing Techniques in Wireless

Dr.T.Purusothaman is currently working as Assistant Professor (RD) in the department of Computer Science and Engineering and Information technology, Government College of Technology, Coimbatore. He has twenty years of teaching experience.He has completed Ph.D in the area of Network Security and Grid Computing.In his thesis, a novel key management scheme was proposed to provide service only for the paid customers in Internet. He has successfully completed a project funded by DIT (Government of India) in the area of cryptanalysis in the year 2006. He has presented a number of papers in various National and International conferences. Many of his papers were published in IEEE Explore. He has to his credit several International Journal Publications in reputed journals including Journal of Grid Computing, Springer. His research interests include Network Security, Grid Computing and Data Mining.

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Figure 4.Dropped bytes - one source and five sinks

Figure 6.Dropped packets sinks

- one source and

five

Figure 5.Dropped bytes - one sink and five sources

Figure 7.Dropped Packets - one sink and five sources

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