Artificial Intelligence based rule base fire engine testing model for congestion handling in opportunistic networks
Ahthasham Sajid, Nighat Usman, Imran Ullah Khan, Saeeda Usman, Aamir Mirza Mehmood, Muhammad Sheraz Arshad Malik, Rana Javed Masood · Measurement and Control · 2020
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.