Abstract:Wireless sensor networks is a system of multiple small sensors limited energy sources, used to sense any given sensing region of interest. In WSN, sensors might be required to work for a longer duration of time from the time of its deployment. Hence to optimize the utilization of energy at each node, we propose an algorithm, which can eventually enhance the lifetime and hence the reliability of the sensor nodes and the overall network. Here PSO based efficient clustering is performed and the appropriate selection of CH and SCH is done. The proposed approach outperforms the tradition F-LEAH algorithm. The result obtained through simulation shows that the proposed algorithm performs better than F-LEACH approach in terms of stability, reduced energy consumption at each node, less delay and drop at the BS and enhanced the lifetime of the network.

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A contactless system became necessary for smart mobility during the COVID-19 pandemic. There are many touchpoints in private and public areas where contact is essential, such as intelligent transportation systems for vaccine carriers, patient ambulances, elevators, metros, buses, hospitals, and banks. A secured contactless device reduces the chances of COVID-19 infection spread. Several devices use smart cards, fingerprint identification, or code-based access. Most of these devices require some form of touch. The cost of such devices varies, depending on their capability and intended use. Sensors developed by using artificial intelligence (AI) to provide secured access are an emerging area. This paper presents an AI-powered contactless face recognition system. The solution has the Internet of Things (IoT) enabled access system. To identify a person, it uses AI assistance for face recognition with the help of Python Dlib’s facial recognition network. Dlib offers a wide range of functionality across several machine learning sectors and is open-source. The Arduino Uno (ATmega328P) and STK500 protocol has been used for communication to testify and validate the performance of the proposed technique. The objective is to detect and recognize faces by the proposed contactless approach. The obtained result shows 92% accuracy, 94% sensitivity, 96% precision and FRR 6% for face detection. There is a significant improvement in FRR in our work compared to the published 27.27%. The implemented solution in this paper provides accurate and secure contactless access to conventional, readily available techniques in public health safety.

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