Artificial Intelligence based rule base fire engine testing model for congestion handling in opportunistic networks. (November 2020)

• Record Type: Journal Article
• Authors: Sajid, Ahthasham; Usman, Nighat; Khan, Imranullah; Usman, Saeeda; Mirza Mehmood, Aamir; Malik, Muhammad Sheraz Arshad; Rana, Javed Masood
• Is Part Of: Measurement and control. Volume 53:Number 9/10(2020)
• Page Start: 1841
• Page End: 1850
• Publication Date: 2020-11
• Subjects: Internet of things -- less congested node -- average congested node -- high congested node -- buffer occupancy -- delay tolerant network; Automatic control -- Periodicals; Engineering instruments -- Periodicals; Production engineering -- Periodicals; 629.8
• DOI: 10.1177/0020294020944965
• Languages: English
• ISSNs: 0020-2940

Abstract

Opportunistic network is emerging as a research domain nowadays with the introduction of Internet of things phenomena. In recent years, storage level congestion issue due to handheld devices is considered as a key challenge to be handled in the opportunistic networks. The prime objective of conducting this research is to develop artificial intelligence rule-based fire engine model to be tested using artificial intelligence latest classification algorithms further implemented using ONE simulator tool over MaxProp protocol. The achieved results show 98% accuracy in terms of classification using k-fold validation technique over six algorithms. The achieved results have been compared with MaxProp protocol over evaluation parameters such as delivery ratio, throughput, routing load, and overhead; whereas delivery ratio increase about 20% for node level and 5% for buffer level and throughput tends to increase 500 and 150 kbps for network and buffer levels, respectively.