PERFORMANCE EVALUATION OF DIRECTED
DIFFUSION DATA DISSEMINATION ROUTING PROTOCOL IN WIRELESS SENSOR NETWORKS
Chetna Sharma Research Scholar
Shivalik Institute of Engineering & Technology Ambala, Haryana, India
Er. Gunjan Gupta Assistant Professor
Shivalik Institute of Engineering & Technology Ambala, Haryana, India
ABSTRACT
The birth of wireless communications dates from the late 1800s, when M.G. Marconi did the pioneer work establishing the first successful radio communication systems have been developing and evolving with a furious pace. In the early stages, wireless communication systems were dominated by military usages and supported accordingly to military needs and requirements. Over the past few years, the world has
become increasingly mobile. As a result traditional ways of networking the world have proven inadequate to meet the challenges posed by our new collective lifestyle. As a result wireless technologies are encroaching on the traditional realm of fixed or wired networks. Recent advances in processing, storage, and communication technologies have advanced the capabilities of small-scale and cost-effective sensor systems, which are composed of a single chip with embedded memory, processor, and transceiver. Wireless sensor networks have a great ability of obtaining data and they can work under any situation, at any time, in any place, which makes it useful in many important fields. So, the military department, industrial circle and academic circle of many countries all over the world are paying great attention to it. It also becomes a hot issue in research at home and abroad today, and it is regarded as one of the ten influencing technology in the 21st century. The aim of the manuscript is to evaluate the performance of directed diffusion data dissemination routing protocol through simulation studies. Then this work will compare directed diffusion protocol with two other traditional data dissemination protocols namely: omniscient multicast and flooding in terms of remaining energy.
Keywords - Wireless Sensor Networks, Routing, Data Dissemination
Introduction
Wireless Sensor Networks (WSNs) [1-5] consists of numerous tiny sensors deployed at high density in regions requiring surveillance and monitoring. These sensors can be deployed at a cost much lower than the traditional wired sensor system. A typical sensor node consists of one or more sensing elements (motion, temperature, pressure, etc.), a battery, and low power radio trans-receiver, microprocessor and limited memory. An important aspect of such networks is that the nodes are unattended, have limited energy and the network topology is unknown. Many design challenges that arise in sensor networks are due to the limited resources they have and their deployment in hostile environments.
Figure 1 Shows the complexity of wireless sensor networks, which generally consist of a data acquisition network and a data distribution network, monitored and controlled by a management center. The plethora of available technologies make even the selection component difficult, let of a consistent, reliable, robust overall system alone the design of consistent, reliable, robust overall system.
Figure 1 : Sensor network [2]
Sensor nodes are deployed in environments where it is impractical or infeasible for humans to interact or monitor them. Some applications require sensors to be small in size and have short transmission ranges to reduce the chances of detection. These size constraints cause further constraints on CPU speed, amount of memory, RF bandwidth and battery lifetime. Hence, efficient communication techniques are essential for
increasing the lifetime and quality of data collection and decreasing the communication latency of such wireless devices.
Unlike the mobile ad hoc networks, sensor nodes are most likely to be stationary for the entire period of their lifetime. Even though the sensor nodes are fixed, the topology of the network can change. During periods of low activity, nodes may go toinactive sleep state, to conserve energy. When some nodes run out of battery power and die, new nodes may be added to the network. Although all nodes are initially equipped with equal energy, some nodes may experience higher activity as result of region they are located in.
An important property of sensor networks is the need of the sensors to reliably disseminate the data to the sink or the base station within a time interval that allows the user or controller application to respond to the information in a timely manner, as out of date information is of no use and may lead to disastrous results.
Another important attribute is the scalability to the change in network size, node density and topology. Sensor networks are very dense as compared to mobile ad hoc and wired networks. This arises from the fact that the sensing range is lesser than the communication range and hence more nodes are needed to achieve sufficient sensing coverage. Sensor nodes are required to be resistant to failures and attacks.
Problem Definition
The aim of current research work is to evaluate the performance of directed diffusion data dissemination routing protocol through simulation studies. Then this work will compare directed diffusion protocol with two other traditional data dissemination protocols namely: omniscient multicast and flooding in terms of remaining energy. This work has been used to compare these three data dissemination protocols on following parameters.
Remaining Energy
Average delay
Distinct –event delivery ratio
Performance Evaluation
This section, presents the simulation parameters and performance metrics, which are used in simulation process.
Simulation Parameters
Following parameters are used in the simulation process.
The number of nodes, which are distributed randomly over a rectangular area of 600m×600m, is 50.
The radio transmission range R is 40-140 m.
MAC: Modified Contention-based MAC
Initial power is 40 joules.
The size of data packet is 64 byte.
Simulation and Results
The impact of remaining energy, average delay and distinct delivery ratio with respect to transmission range on the entire network is shown from the figures, for directed diffusion and two other schemes namely omniscient multicast and flooding respectively.
Figure 2 : Remaining Energy for Direct Diffusion, Omniscient and Flooding
As from figure 5.10, this is depicted that remaining energy is good in direct diffusion.
As in case of flooding there is variation when transmission range reaches at 80 and again at 120. But in case of flooding remaining energy decreases when transmission range exceeds 120 meters. And in case of omniscient the remaining energy remains almost constant . There is slight less change in this. Direct Diffusion performs the best. There is fall in remaining energy at 120. In contrast to the omniscient multicast and flooding the remaining energy is higher in case of direct diffusion.
Figure 3 : Average Delay for Direct Diffusion, Omniscient and Flooding
As figure 5.11 depicts about average delay for direct diffusion, omniscient, and flooding. In this, as message to be delivered or you can say connectivity among the different nodes increases, the time delay increases for Direct Diffusion.
In case of flooding this is lowest as the message is broadcast in case of flooding.
And omniscient has the performance next to flooding in case of average delay.
Figure 4 : Distinct Event Delivery Ratio for Direct Diffusion, Omniscient and Flooding
From figure 5.12 this is clear that Distinct Event Delivery Ratio (DEDR), for direct diffusion is highest among the other two protocol. This goes high with respect to increase in transmission range.
CONCLUSION AND FUTURE SCOPE
In WSNs to meet the new challenges; innovative protocols and algorithms are needed to achieve energy efficiency, flexible scalability and adaptability and good networks performance. Efficient data dissemination is a big challenge for wireless sensor networks.
Directed diffusion protocol solves many problems of efficient data dissemination for wireless sensor networks.
Directed diffusion has several key features and it differs from traditional networking.
First, directed diffusion is data-centric; all communication in a directed i.e. diffusion-based sensor network uses interests to specify named data. Second, all communication in directed diffusion is neighbor-to-neighbor, unlike the end-to-end communication in traditional data networks. In other words, every node is an end in a sensor network. In the sense, there are no routers in a sensor network. Each sensor node can interpret data and interest messages. This design choice is justified by the task specificity of sensor networks. Sensor networks are not general-purpose communication networks. Third, sensor nodes do not need to have globally unique identifiers or globally unique addresses.
Nodes, however, do need to distinguish among neighbors.
The present research work describes the performance evaluation done for the Data Dissemination Protocols for wireless sensor networks using NS-2 simulator. Directed diffusion is a data dissemination routing protocol, which aims at reducing the energy consumption of WSNs nodes, robust communication thus increasing the overall network lifetime. This evaluation indicates that directed diffusion can achieve significant energy savings and can outperform idealized traditional schemes omniscient multicast, flooding in terms of the different changes of the entire network remaining energy with the time changing on the same simulation environment.
So direct diffusion, omniscient and flooding are simulated and evaluated. The performance for three of these protocols is evaluated against remaining energy, average delay and distinct event delivery ratio which shows that direct diffusion gives better results. Further, in this direction future work can be done to evaluate by considering another parameters like system lifetime, traffic load distribution through simulation.
REFERENCES
[1] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless Sensor Networks: A Survey,” Journal of Computer Networks 2002, vol.38, pp.393–422, 2002.
[2] F.L.Lewis, “Wireless Sensor Networks,” to appear in smart Environments,
Technologies, Protocols, and Applications 2004, New York, pp.1-18, 2004.
[3] N.Jamal, A.E.Kamal, “Routing Techniques in Wireless Sensor Networks,”
Proceeding of the Wireless Communications, IEEE 2004, vol.11, issues.6, pp.6-28, December 2004.
[4] K.Akkaya, M.Younis, “A survey on routing protocols for Wireless Sensor Networks,” Journal of Ad-Hoc Networks 2005, vol.3, pp.325-349, 2005.
[5] P.Jiang, Y.Wen, J.Wang, X.Shen, A.Xue, “Study of Routing Protocol in Wireless Sensor Networks,” Proceeding of the IEEE 6th World Congress on Intelligent Control and Automation 2006,China, vol.1, pp.266-270, June 2006.
[6] D. Estrin, L. Girod, G. Pottie, M. Srivastava, “Instrumenting the world with wireless Sensor networks,” Proceeding of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2001), salt Lake City, Utah, vol.4, pp.2033-2036, May 2001.
[7] C.Mallanda, S. Basavaraju, A. Kulshrestha, R. Kannan, A.Durresi and S. S.
Iyengar, "Secure Cluster Based Energy Aware Routing for Wireless Sensor Networks,” Proceeding of the International Conference on Wireless Networks ICWN 2004, pp.461-466, 2004.
[8] S.Sastry and S.S. Iyengar, “Real-time Sensor-Actuator Networks,” International Journal of Distributed Sensor Networks 2004, vol.1, no.1, pp.17-34, 2004.
[9] S.Sastry and S.S. Iyengar, “Sensor Technologies for Future Automation Systems,” Journal of Sensor Letters 2004, vol. 2, no.1, pp. 9–17, March 2004.
[10] S. S. Iyengar and R. R. Brooks, “Distributed Sensor Networks,” CRC press Inc, 2004, pp.1128 Dec 28, 2004.
[11] A. Bharathidasan, V. AnandSaiPonduru, “Sensor Networks: An overview,”
Technical report, University of California, Davis, 2002.
[12] N. Bulusu, D. Estrin, L. Girod and J. Heidemann, “Scalable Coordination for wireless sensor networks: Self-Configuring Localization Systems,” Proceeding of The Sixth International Symposium on Communication Theory and applications
(ISTCA 2001), Amble side, Lake District, UK, July 2001.
[13] D.Estrin, R. Govindan, J. Heidemann and S. Kumar, “Next century challenges:
Scalable Coordination in Sensor Networks,” Proceeding of the 5th Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom’1999), Seattle, WA, pp.263-270, August 1999.
[14] W. Heinzelman, J. Kulik, and H. Balakrishnan, “Adaptive protocols for information dissemination in wireless sensor network,” Proceeding of the 5th Annual ACM/IEEE International Conference on Mobile computing and Networking (MobiCom’1999), Seattle, WA, and pp.174-185, August 1999.
[15] C.Intanagonwiwat, R. Govindan and D. Estrin, “Directed diffusion: A scalable and robust communication paradigm for sensor networks,” Proceeding of the 6th Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom’2000), Boston, MA, pp.56-67, August 2000.
[16] N. Bulusu, J. Heidemann, and D. Estrin, “GPS-Less Low Cost Outdoor Localization for Very Small Devices,” Proceeding of the Personal Communications Magazine, IEEE, vol.7, no. 5, pp. 28–34, October 2000.
[17] J. Heidemann, F. Silva, C. Intanagonwiwat, R. Govindan, D. Estrin, and D.Ganesan, “Building Efficient Wireless Sensor Networks with Low-level Naming,” Proceeding of the Symposium on Operating Systems Principles, Banff, Alberta, Canada, 2001, pp.146–159, October 2001.
[18] J.Kulik, W. R. Heinzelman, and H. Balakrishnan, “Negotiation-based protocols for disseminating information in wireless sensor networks,” Journal of Wireless Networks 2002, vol.8, pp.169-185, March 2002.
[19] F. Ye, H. Lou, J. Cheng, S. Lu and L. Zhang, “A two tier data dissemination model for large-scale wireless sensor networks,” Proceeding of the ACM/IEEE MOBICOM, 2002, pp.148–159, September 2002.
[20] C. Intanagonwiwat, R. Govindan, D. Estrin, J. Heidemann, F.Silva, "Directed Diffusion for Wireless Sensor Networking,” IEEE/ACM Transaction Networking
2003, vol.11, no.1, pp.2-16, February 2003.
[21] S. Ratnasamy, B. Karp, S. Shenker, D. Estrin, R. Govindan, L. Yin and F. Yu,
“Data-centric Storage in Sensor nets with GHT, a Geographic Hash Table,”
Mobile Networks and Applications, vol. 8, pp. 427–442, August 2003.
[22] M. Ditzel, K. Langendoen,“D3: Data –Centric Data Dissemination in wireless sensor networks,” Proceeding of the 8th European Conference on Wireless Technology 2005, Paris, pp.185-188, 2005.
[23] N. Hu, D. Zhang, “Source Routing Directed in Wireless Sensor Networks,”
Journal of Information Technology 2006, vol.5, no.3, pp.534-539, 2006.
[24] L. Zhiyu, S. Haoshan, “Design of Gradient and Node Remaining Energy Constrained Directed Diffusion Routing for WSN,” International Conference on Wireless Communications, Networking and Mobile Computing, 2007,vol-27, pp.2600-2603, September 2007.
[25] C.Zhi-yan, J.Zhen-Zhou, H.Ming-zeng, “An energy-aware broadcast scheme for directed diffusion in wireless sensor network,” Journal of Communication and Computer, vol. 4, no.5 pp.28-35, May 2007.
[26] Z.Zi-mingl, Z.Yuan-yuan2, “Topology control for directed diffusion in wireless sensor networks,” Journal of Communication and Computer 2008, vol. 5, no.1, pp.9-16, January 2008.
[27] K. Casey, R.Neeliseti, A.Lim, “RTDD: A Real-Time Communication Protocol For Directed Diffusion,” Proceeding of the IEEE Wireless Communications and Networking Conference, WCNC 2008, pp.2852-2857, April 2008.
[28] The Network Simulator NS-2, http://www.isi.edu/nsnam/ns
[29] The REAL network simulator, http://www.cs.cornell.edu/skeshav/real [30] http://www.isi.edu/nsnam/nam/ NAM Network Animator
[31] http://www.isi.edu/nsnam/xgraph/ Xgraph homepage
[32] http://www.geocities.com/tracegraph/ Trace Graph homepage
[33] NS-2 Documentation, http://www.isi.edu/nsnam/ns/ns-documentation.html
