Total views : 165
Wireless Networks Throughput Enhancement Using Artificial Intelligence
Objectives: To utilize the correct technique of artificial intelligence in multi-channel network. Multi-channel is used to reduce interference and improve performance. Methods/Statistical Analysis: In this work a simple method has been developed that is a positive contribution to the various solutions that already exists. Overall, the methodology adopted in this work consists of three stages. The first step is to create a model for specific wireless environment, the second step is to choose the right tool to optimize the performance and the third step is the careful selection of performance indicators for routing improvements. Findings: We developed an agent model that is inspired from the communication and evaluative features of a honey bee colony. The bee agents in our model have a simple behavior. As a result, the algorithm is able to take routing decisions in a decentralized and asynchronous fashion. We have conducted extensive simulations in MATLAB to show the advantages of our algorithm over existing state-of-the-art routing algorithms developed by Nature inspired routing community. The candidate algorithm is able to achieve better performance values with a simple agent model. Application/Improvements: The proposed protocol discovers and evaluates multiple-paths in a deterministic fashion by utilizing a variant of breadth first search. It will increase the output of our resulting routing protocol.
Artificial Intelligence, Routing, Throughput,Wireless Networks.
- Akyildiz IF. Next generation/dynamic spectrum access/ cognitive radio wireless networks: a survey. Computer Networks. 2006; 50(13): 2127–59.
- Suh C, Mir ZH, Young-Bae Ko. Design and implementation of enhanced IEEE 802.15. 4 for supporting multimedia service in Wireless Sensor Networks. Computer Networks. 2008; 52(13): 2568–81.
- Levy S. Artificial life: A report from the frontier where computers meet biology. Vintage Books; 1993.
- Manoj BS. On the use of higher layer information for cognitive networking, in IEEE Global Telecommunications Conference (GLOBECOM); 2007. p. 3568–73.
- Ahmad S, Bilal Ehsan. The cloud computing security secure user authentication technique (Multi Level Authentication), IJSER. 2013; 4(12): 2166–71.
- Ahmad S, Hussain S, Iqbal MF. A formal model proposal for wireless network security protocols. Science International. 2015; 27(3):.
- Brayton R, Mishchenko A. ABC: An academic industrialstrength verification tool. Computer Aided Verification. Springer Berlin Heidelberg: 2010. 6174, p. 24–40.
- Ahmad S. Formal methods and network security protocols: A Survey. Science International. 2017; 29(4): 581–-858.
- Raychaudhuri D, Mandayam NB, Evans JB, Ewy BJ, Seshan S, Steenkiste. CogNet: an architectural foundation for experimental cognitive radio networks within the future internet, in 1st ACM/IEEE Int. Workshop on Mobility in the Evolving Internet Architecture, (San Francisco, California) ACM, 2006.p. 1116.
- Muhammad N. A comparison of big data and cloud computing applications with prospective to software engineering. Science International. 2017; 29(3): 1–8.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.