Predicting types of failures in wireless sensor networks using an adaptive neuro-fuzzy inference system

Lim, Cheng Leong; Goh, Cindy; Khan, Asiya; Syed, Aly; Li, Yun

doi:10.1109/wimob.2016.7763207

Cited by 1 publication

(1 citation statement)

References 25 publications

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“…To the best of our knowledge, there are few or no research papers on the use of ANFIS in SDN and IPv6 network migration, yet ANFIS has a wide range of multidimensional implementations and applications. We went through some literature of ANFIS implementations in communication networks, at which it is mostly used in estimation, prediction, optimization, and forecasting [ 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ], but none of these studies are particularly related to SDN and IPv6 network migration.…”

Section: Background and Related Workmentioning

confidence: 99%

Intelligent Approach to Network Device Migration Planning towards Software-Defined IPv6 Networks

Dawadi

Rawat

Joshi

et al. 2021

Sensors

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Internet and telecom service providers worldwide are facing financial sustainability issues in migrating their existing legacy IPv4 networking system due to backward compatibility issues with the latest generation networking paradigms viz. Internet protocol version 6 (IPv6) and software-defined networking (SDN). Bench marking of existing networking devices is required to identify their status whether the existing running devices are upgradable or need replacement to make them operable with SDN and IPv6 networking so that internet and telecom service providers can properly plan their network migration to optimize capital and operational expenditures for future sustainability. In this paper, we implement “adaptive neuro fuzzy inference system (ANFIS)”, a well-known intelligent approach for network device status identification to classify whether a network device is upgradable or requires replacement. Similarly, we establish a knowledge base (KB) system to store the information of device internetwork operating system (IoS)/firmware version, its SDN, and IPv6 support with end-of-life and end-of-support. For input to ANFIS, device performance metrics such as average CPU utilization, throughput, and memory capacity are retrieved and mapped with data from KB. We run the experiment with other well-known classification methods, for example, support vector machine (SVM), fine tree, and liner regression to compare performance results with ANFIS. The comparative results show that the ANFIS-based classification approach is more accurate and optimal than other methods. For service providers with a large number of network devices, this approach assists them to properly classify the device and make a decision for the smooth transitioning to SDN-enabled IPv6 networks.

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