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Smart home appliances like smart door, smart AC, smart lights etc., are the facilities which can be utilized because of advancements in Internet of Things. However, security of these smart home appliances is very much important to avoid security threats. Existing smart home appliances are facing problems with security issues such as such as hacking, mishandling of devices etc., due to one stage authentication process. The proposed Secured Smart Mobile App for Smart Home Environment (SSMASHE) provides a two stage authentication process which can secure smart home appliances. It consists of three modules, first one is smart things, next one smart mobile app and the third module is big data platform. In this paper a smart home environment embedding with smart mobile app has been simulated to evaluate the security mechanism through authentication process. In this process the delay has been calculated for the purpose of fast communication and security assigned to big data environment. The proposed method delay time is less when compared to Fast Identity Online (FIDO) method. The proposed method is comfortable to real time heterogeneous smart home IoT devices because this process follows the asynchronous authentication procedure.

The number of connected IoT devices deployed globally is increasing at a tremendous rate. The IoT has a wide range of application domains providing extensive IoT-based services. The basic idea is to deliver a new set of applications where smart devices collaborate without any human interference. The variability and visibility of IoT based services lead to the development of the full spectrum of protocols. With the exponential growth of applications, it is essential to analyze the existing application layer protocols being used to exchange information among devices. In this paper, a detailed analysis of existing popular application layer protocols like Constrained Application Protocol (CoAP), Message Queue Telemetry Transport (MQTT), Advance Message Queuing Protocol (AMQP), Extensible Messaging and Presence Protocol (XMPP), etc. has been done to categorize them based on well-known properties such as architecture, energy consumption, reliability, QoS, and security aspects.

Currently, the world is transforming to smart world with the help of constant network of Internet of Things (IoT) objects or devices in all aspects of human life. Smart health care is one of the major fields among all the IoT applications. Remote monitoring of patient includes the opportunities for hospitals to minimize the cost and improve the quality of treatment for patients and family members. In this paper, IoT healthcare security and interoperability (IHSI) approach has been proposed to maintain and monitor the patients’ health conditions continuously with remote IoT medical devices, which equipped with sensors and actuators. Moreover, the IoT technologies in health care are smartwatches, fitness bands, contact lenses, wireless sensors and microchips under the skin. IHSI approach has four modules such as authentication, encryption, operational efficiency and automatic data entry. In this paper communication cost, computational cost, throughput, and end-to-end delay are the security measurements which have been taken to find the security efficiency. Furthermore, a detailed comparison analysis has been done among the IHSI approach and other related recent approaches; as a result the proposed approach provides good security, less computation and communication costs, higher throughput, and lesser end-to-end delay.

This study is intended to investigate the volatility patterns in Bombay Stock Exchange Limited Sensitivity Index (BSE Sensex) based on time series data collected for 10 years period of time. To reach out the predefined objectives of the study, the authors have employed generalized autoregressive conditional heteroscedastic models. The study revealed that the presence of heteroscedasticiy is found in BSE Sensex. Further, the model produced highly accurate results when the researchers compared the estimated results from actual. Furthermore, the volatility of BSE Sensex has shown the features of clustering and significant time varying. Moreover, the model has indicated that there is a positive correlation between daily stock returns and the BSE Sensex volatility.