Development of Trustworthy Self-adaptive Framework for Wireless Sensor Networks

Habib, Sami J.; Marimuthu, Paulvanna Nayaki

doi:10.1007/978-3-030-45691-7_34

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…Our literature survey revealed that self-awareness was considered as a preliminary requirement in building secure and optimized systems (Gelenbe et al, 2020), fog and mist computing systems (Preden et al, 2015), self-adaptive and trustworthy computing systems (Habib and Marimuthu, 2020), and healthcare IoT (Karmani et al, 2019). In this work, we have derived a self-aware model, which would be aware of the defined uncertainties and their impacts on WSN data transmission.…”

Section: Related Workmentioning

confidence: 99%

A Fuzzy Framework for Self-Aware Wireless Sensor Networks

Habib¹,

Marimuthu²

2022

JER is an international, peer-reviewed journal that publishes f

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We have developed a fuzzy logic framework to simulate self-awareness in wireless sensor networks (WSNs) that envisages data transmission losses due to abnormalities in the operating environment. Fuzzy logic is selected to model the awareness, because it requires less data to encode, and occupies less memory, making it suitable for sensor nodes with limited resources. We view WSN as a multi-layered infrastructure, where a central-server in the top-layer manages the gateways in the intermediate-layer, and the gateways are aggregating the raw sensed data from the bottom layer’s sensors. The self-awareness is simulated using Mamdani Fuzzy inference system (FIS) that utilizes a custom-defined membership functions of the selected parameters, such as temperature, humidity, wind speed, and battery residual-level to guide WSN through its data transmissions. The input and output parameters are defined with three linguistic variables (low, medium and high) and a predetermined set of rules is applied to the selected FIS to determine the impact level. The impact is mapped into packet error rate and the gateway then decides whether to carry out the data transmissions while being aware of the selected uncertainties and their impact. The experimental results on a multivariate dataset integrating real-time meteorological data with randomly generated battery voltage revealed that the proposed FIS framework demonstrated 38% energy savings by non-executing unworthy communications against normal transmissions.

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