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Every industry in the world is getting equipped with smart devices. The healthcare industry is enhancing its services via various smart wearable devices. The transport industry is developing customer satisfaction and goods security with the use of the intelligent transport system. The agriculture industry is pressing towards smart farming, like using sensors to improve the irrigation system and farm monitoring, and many more such domains are aiming towards an automated and smart environment. The education system is also leaping forward by introducing the concept of a smart university. Internet of Things (IoT) will play a crucial role in the pursuit of worldwide connectivity. In this paper, we have broadly discussed the basics of IoT, its applications, and various protocols and databases used. We have provided a brief insight into myriad of IoT applications, namely Smart cities, Smart security, Smart agriculture, Home automation, Smart health, Industrial control, Smart cars, and Smart university. Also, a comparison of protocols-CoAP, XMPP and MQTThas been highlighted. For database connectivity, comparison among MongoDB, SQLite, and Firebase has been discussed. We aim to provide a succinct survey to all those who want to use IoT predominantly and specifically according to their requirements.

The lens-free shadow imaging technique (LSIT) is a well-established technique for the characterization of microparticles and biological cells. Due to its simplicity and cost-effectiveness, various low-cost solutions have been developed, such as automatic analysis of complete blood count (CBC), cell viability, 2D cell morphology, 3D cell tomography, etc. The developed auto characterization algorithm so far for this custom-developed LSIT cytometer was based on the handcrafted features of the cell diffraction patterns from the LSIT cytometer, that were determined from our empirical findings on thousands of samples of individual cell types, which limit the system in terms of induction of a new cell type for auto classification or characterization. Further, its performance suffers from poor image (cell diffraction pattern) signatures due to their small signal or background noise. In this work, we address these issues by leveraging the artificial intelligence-powered auto signal enhancing scheme such as denoising autoencoder and adaptive cell characterization technique based on the transfer of learning in deep neural networks. The performance of our proposed method shows an increase in accuracy >98% along with the signal enhancement of >5 dB for most of the cell types, such as red blood cell (RBC) and white blood cell (WBC). Furthermore, the model is adaptive to learn new type of samples within a few learning iterations and able to successfully classify the newly introduced sample along with the existing other sample types.

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