Dynamic redundancy management of multipath routing for intrusion tolerance in heterogeneous wireless sensor networks

4. SOFTWARE USED:

4.1. Ns2:

NS2 is an open-source event-driven simulator designed specifically for research in computer communication networks. Since its inception in 1989, NS2 has continuously gained tremendous interest from industry, academia, and government.

Having been under constant investigation and enhancement for years, NS2 now contains modules for numerous network components such as routing, transport layer protocol, application, etc. To investigate network performance, researchers can simply use an easy-to-use scripting language to configure a network, and observe results generated by NS2. Undoubtedly, NS2 has become the most widely used open source network simulator, and one of the most widely used network simulators.

Unfortunately, most research needs simulation modules which are beyond the scope of the built-in NS2 modules. Incorporating these modules into NS2 requires profound understanding of NS2 architecture. Currently, most NS2 beginners rely on online tutorials. Most of the available information mainly explains how to configure a network and collect results, but does not include sufficient information for building additional modules in NS2. Despite its details about NS2 modules, the formal documentation of NS2 is mainly written as a reference book, and does not provide much information for beginners. The lack of guidelines for extending NS2 is perhaps the greatest obstacle, which discourages numerous researchers from using NS2. At this moment, there is no guide book which can help the beginners understand the architecture of NS2 in depth. The objective of this textbook is to act as a primer for NS2 beginners. The book provides information required to install NS2, run simple examples, modify the existing NS2 modules, and create as well as incorporate new modules into NS2. To this end, the details of several built-in NS2 modules are explained in a comprehensive manner.

4.1.2. Cygwin:

Cygwin is a Unix-like environment and command-line interface for Microsoft Windows. Cygwin provides native integration of Windows-based applications, data, and other system resources with applications, software tools, and data of the Unix-like environment. Thus it is possible to launch Windows applications from the Cygwin environment, as well as to use Cygwin tools and applications within the Windows operating context.

Cygwin consists of two parts: a dynamic-link library (DLL) as an API compatibility layer providing a substantial part of the POSIXAPI functionality, and an extensive collection of  software tools and applications that provide a Unix-like look and feel.

Cygwin was originally developed by Cygnus Solutions, which was later acquired by Red Hat. It is free and open source software, released under the GNU General Public License version 3. Today it is maintained by employees of Red Hat, Net App and many other volunteers.

4.1.3. Description:

Cygwin consists of a library that implements the POSIX system call API in terms of Win32 system calls, a GNU development tool chain (including GCC and GDB) to allow software development, and a large number of application programs equivalent to those on Unix systems. Programmers have ported many Unix, GNU, BSD and Linux programs and packages to Cygwin, including the X Window System, K Desktop Environment 3, GNOME, Apache, and TeX. Cygwin permits installing inetd,syslogd, sshd, Apache, and other daemons as standard Windows services, allowing Microsoft Windows systems to emulate Unix and Linux servers.

Cygwin programs are installed by running Cygwin's "setup" program, which downloads the necessary program and feature package files from repositories on the Internet. Setup can install, update, and remove programs and their source code packages. A complete installation will take in excess of 17 GB of hard disk space, but usable configurations may require as little as 1 or 2 GB.

Fig.3 Cygwin Execution Window.

OUTPUT AND GRAPHS

Fig .4: No.of Packetdelivery Ratio vs number of nodes

The Packet delivery ratio is the ratio of the data packets delivered to the destination successfully. The Packet delivery ratio is one of the important parameter to evaluate the quality of the network.

The formula used to find the Packet delivery ratio is as follows:

The fig .4: graph shows that, the proposed scheme provides high performance when increasing the no. of nodes too. Above figure gives the comparison analysis output of the proposed scheme with the existing approach.

Fig .5: Packetdloss Ratio vs time

The fig.5: shows that the Packet loss ratio is used to evaluate the quality of the network provided by the routing scheme. The packet loss ratio of the proposed scheme is compared with the existing approach. The packet loss ratio of the proposed scheme is lower than the existing scheme as shown in above graph. Lower the Packet loss ratio indicates that the high performance of the network.

Fig .6: Residual energy vs simulation period

The fig.6: shows that the energy consumption of a node decides the life time of the node. The residual energy is calculated by using the following formula

Where,

Total Energy

n Number of Transmission

Transmission Power

The above graph shows that the proposed scheme consumes less energy than the existing scheme. It indicates that the proposed scheme provides the higher battery lifetime.

Fig .7:Throughput vs simulation period

The fig.7: shows that the Throughput is the amount of packets delivered to the destination per unit of time. The Throughput is calculated by using the formula

The system provides high throughput while the node is moving when compared with the proposed scheme than the existing approach. The comparative analysis of the proposed scheme with the existing approach is given by the graph.

CONCLUSION :

We have performed the tradeoff analysis of energy consumption vs. QoS gain in reliability, timeliness, and security for redundancy management of clustered heterogeneous wireless sensor networks utilizing multipath routing to answer user queries. Also, developed a novel probability model to analyze the best redundancy level in terms of path redundancy and source redundancy, as well as the best intrusion detection settings in terms of voters and the intrusion invocation interval under which the lifetime of a heterogeneous wireless sensor network is maximized while satisfying the reliability, timeliness and security requirements of query processing applications in the presence of unreliable wireless communication and malicious nodes. Finally,applied our analysis results to the design of a dynamic redundancy management algorithm to identify and apply the best design parameter settings at runtime in response to environment changes to prolong the system lifetime.

REFERENCES:

[1] O. Younis and S. Fahmy, “HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks,” IEEE Trans. Mobile Computing., vol. 3, no. 4, pp. 366–379, 2004.

[2] E. Felemban, L. Chang-Gun, and E. Ekici, “MMSPEED: multi path multi-SPEED protocol for QoS guarantee of reliability and timeliness in wireless sensor networks,” IEEE Trans. Mobile Computing., vol. 5, no.6, pp. 738–754, 2006.

[3] I. R. Chen, A. P. Speer, and M. Eltoweissy, “Adaptive fault-tolerant QoS control algorithms for maximizing system lifetime of query-based wireless sensor networks,” IEEE Trans. Dependable Secure Computing, vol. 8, no. 2, pp. 161–176, 2011.

[4]M. Yarvis, N. Kushalnagar, H. Singh, A. Rangarajan, Y. Liu, and S.Singh, “Exploiting heterogeneity in sensor networks,” in Proc. 2005IEEE Conference. Computer Communication., vol. 2, pp. 878–890.

[5] H. M. Ammari and S. K. Das, “Promoting heterogeneity, mobility and energy-aware diagram in wireless sensor networks,” IEEE Trans. Parallel Distribution System, vol. 19, no. 7, pp. 995–1008, 2008.

[6] X. Du and F. Lin, “Improving routing in sensor networks with heterogeneous sensor nodes,” in Proc. 2005 IEEE Technology Conference, pp.2528–2532.

[7] S. Bo, L. Osborne, X. Yang, and S. Guizani, “Intrusion detection techniques in mobile ad hoc and wireless sensor networks,” IEEE Wireless Communication. Mag., vol. 14, no. 5, pp. 560–563, 2007.

[8] I. Krontiris, T. Dimitriou, and F. C. Freiling, “Towards intrusion detection in wireless sensor networks,” in Proc. 2007 European Wireless Conference.

[9] J. H. Cho, I. R. Chen, and P. G. Feng, “Effect of intrusion detection on reliability of mission-oriented mobile group systems in mobile ad-hoc networks,” IEEE, vol. 59, no. 1, pp. 231–241, 2010.

[10] A. P. R. da Silva, M. H. T. Martins, B. P. S. Rocha, A. A. F. Loureiro, L.B. Ruiz, and H. C. Wong, “Decentralized intrusion detection in wireless sensor networks,” in Proc. 2005 ACM Workshop Quality Service Security Wireless Mobile Network.

Directory: admin -> resources -> project -> paper
paper -> Survey of manet based on Routing Protocols
paper -> Mimics the movement of Real Insects
paper -> Dynamic algorithm for Load balancing in a hierarchical Cloud Computing Network Pinki 1, Nida2
paper -> Enhancement of Resource Recommendation System with Graph Based Using som sandeep Pradhan Mr. Sandeep Gonnade
paper -> Android Operating System
paper -> Big Data: Applications in Governmental Services Ms. Mamta Arora
paper -> Digital image watermark key extraction with Encryption and Decryption Scheme in matlab
paper -> Automatic Spray Painting
paper -> A review paper on different routing protocols of vanets
paper -> Human Action Recognition