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Summary: Internet of things (IoT) has evolved as an innovation of next generation in this world of smart devices. IoT tends to provide services for data collection, data management, and data and device security required for application development. Things or devices in IoT communicate and compute to make our lives comfortable and safe. In inventory automation, real‐time check on items, their information management, and status management, monitoring can be carried out using IoT. The huge amount of data that flows among the devices in the network demands for a security framework that ensures authentication, authorization, integrity, and confidentiality of data. The existing security solutions like SIMON or SPECK offer lightweight security solutions but are vulnerable to differential attack because of their simplicity. Moreover, existing solutions do not offer inbuilt authentication. Therefore, this research work contributes a secure and lightweight IoT‐based framework using wireless sensor network (WSN) as a technology. The existing security solutions SPECK and SIMON are compared with the proposed security approach using COOJA simulator. The results show that proposed approach outstands others by 2% reduction in number of CPU cycles, 10% less execution time, 4% less memory requirements of security approach, and with minimum 10% more security impact. [ABSTRACT FROM AUTHOR]

In the realm of optoelectronic applications, the incorporation of a metal interlayer between layers of a topological material holds significant potential. The interlayer introduces benefits such as improved charge transfer efficiency, tunable band alignment, plasmonics effect, and enhanced light‐matter interactions, paving the way for more efficient and versatile optoelectronic devices. This work explores the incorporation of gold between Bi2Se3, resulting in an enhancement in the response time of photoresponse of interlayered systems (Bi2Se3‐Au‐Bi2Se3). The introduction of a gold interlayer between Bi2Se3 significantly influences the overlayer structure, crystallinity, crystallite size, and even phonon behavior. Moreover, the responsivity of the interlayered device at a bias (2 V) is found to have high responsivity with fast response and decay time (τr = 0.1 ms, and τd = 24 ms of interlayered systems) as compared to intrinsic Bi2Se3(τr = 0.6 ms, and τd = 28 ms). This change is attributed to the bandgap alteration of the Bi2Se3/Au interface, which is monitored through transient spectroscopy performed at different electric biases. This study suggests that incorporating a gold interlayer holds potential benefits for various optoelectronic applications. [ABSTRACT FROM AUTHOR]

Summary: In the development of smart cities, the intelligent transportation system (ITS) plays a major role. The dynamic and chaotic nature of the traffic information makes the accurate forecasting of traffic flow as a challengeable one in ITS. The volume of traffic data increases dramatically. We enter the epoch of big data. Hence, a 1deep architecture is necessary to process, analyze, and inference such a large volume of data. To develop a better traffic flow forecasting model, we proposed an attention‐based convolution neural network long short‐term memory (CNN‐LSTM), a multistep prediction model. The proposed scheme uses the spatial and time‐based details of the traffic data, which are extracted using CNN and LSTM networks to improve the model accuracy. The attention‐based model helps to identify the near term traffic details such as speed that is very important for predicting the future value of flow. The results show that our attention‐based CNN‐LSTM prediction model provides better accuracy in terms of prediction during weekdays and weekend days in the case of peak and nonpeak hours also. We used data from the largest traffic data set the California Department of Transportation (Caltrans) for our prediction work. [ABSTRACT FROM AUTHOR]