G. Suman Shashank

Bio: G. Suman Shashank is an academic researcher. The author has contributed to research in topics: Wireless network & Wireless sensor network. The author has co-authored 1 publications.

Multicriteria decision examination in wireless sensor networks

Abstract: The role of wireless sensor networks (WSNs) is essential to the present scenario. Numerous independent sensors exist in a WSN that cover the entire terrain and have the task of inspecting a specific phenomenon. For this reason, the sensors must work with minimal constraints, such as least energy consumption and data memory. To achieve the objective, many techniques are applied, one of which is clustering, where nodes are congregated into clusters and a head node is elected. Suitable cluster head selection is crucial for improving the energy management of a sensor network. In this paper, a technique based on a dominance based rough set is introduced for cluster head selection on the basis of attributes that have the capacity to augment the sustainability and lifetime of the sensor network. This method is far superior to the probabilistic approach of cluster head selection in a WSN.

Decision Model Applied in IoT for Green Buildings Based on Grey Incidence Analysis and ANN

Abstract: Due to the fewer uncertainty samples and information-lacking problems in the decision-making center of the Internet of Things (IoT) for green buildings, the optimized model was selected as the preferred method in settlement prediction. In this paper, we proposed the adaptive adjustment strategies of the application layer in IoT for green buildings based on grey incidence analysis and artificial neural networks (ANNs). An additional layer H fuzzy propagation natural network algorithm was introduced to collect sensing layer data of IoT and adaptively adjust decisions. The energy-saving control of the building needs to be adjusted continuously; therefore, we have taken a grey incidence evaluation to obtain adjustment of the parameters. At the same time, the actual Heating Ventilation Air Conditioning subsystem is often in the grey state above, and the current control system of its system is missing the corresponding adjustment scheme. The introduction of the data evaluation in the data center for adaptive adjustment of input data is an effective solution. The real-time running result shows that the proposed solution reduces energy consumption by over 30% compared to the state-of-the-art approaches while having on average 10% fewer expired measurements. The strategies have a significant impact on energy savings for green buildings.