Due to Internet of Things (IoT), it has become easy to surveil the critical regions. Images are important parts of Surveillance Systems, and it is required to protect the images during transmission and storage. These secure surveillance frameworks are required in IoT systems, because any kind of information leakage can thwart the legal system as well as personal privacy. In this paper, a secure surveillance framework for IoT systems is proposed using image encryption. A hyperchaotic map is used to generate the pseudorandom sequences. The initial parameters of the hyperchaotic map are obtained using partial-regeneration-based non-dominated optimization (PRNDO). The permutation and diffusion processes are applied to generate the encrypted images, and the convolution neural network (CNN) can play an essential role in this part. The performance of the proposed framework is assessed by drawing comparisons with competitive techniques based on security parameters. It shows that the proposed framework provides promising results as compared to the existing techniques.

This research suggests a comprehensive mathematical modelling approach for the efficient processing of signals in real-time within Internet of Everything (IoE) environments. The model employs state-of-the-art methods in signal processing, control theory, and machine learning to address the challenges posed by the constantly changing and diverse IoE networks. The proposed architecture employs real-time data processing and collection to improve the overall resilience of the network, increase the accuracy of decision-making, and improve system performance. Adaptive filtering strategies were implemented in this investigation to maintain signals in dynamic network environments. Additionally, robust algorithms were developed and executed for anomaly identification, data compression, and noise reduction. The proposed model's potential to significantly enhance the capabilities of IoE systems is demonstrated through extensive simulations and real-world case studies, which serve to assess its utility.

The suggested research is based on artificial intelligence for the enhancement of flood analysis and disaster management through incorporating deep learning. Thus, relying on powerful Google’s Earth Engine as a cloud-based environment for analysis of geospatial data, we designed a model on the basis of satellite imagery, climate data and past floods to predict future ones. The chaotic interactions between the environment and floods are analyzed through feed forward deep learning model of CNN and LSTM with the distinction presented in details as follows. It assist the authorities in making proper projections on floods occurrences and therefore mitigate the effects of such disasters wherever they occur. The results revealed here strengthen the proposed method to attain a better solution than the other methods with the higher prediction rate. The above proposed system of AI flood prediction has the use in managing the losses that may be caused by floods, and in this way, may help to prevent the loss of many lives, therefore making it a crucial invention in the eventuality of disasters.

2.7 Thinger Io - More explanation needed with fig. Add more sensors which has been used by the other researchers in their work. Add future scope at the end.

Quantum computers can solve difficult optimization issues, unlike regular computers. The proposed system optimizes smart grid energy distribution, load balancing, and resource allocation using quantum annealing and Grover's method. Quantum optimization should boost processing speed and accuracy. Quantum algorithms optimize electricity flow, mitigate transmission loss, and boost grid efficiency. By monitoring real-time data and changing loads, dynamic load balancing reduces smart grid bottlenecks and optimizes resource utilization. Machine learning algorithms will precisely forecast energy demand, enhancing grid control and resource distribution. Quantum computing and machine learning enhance smart grid management. From this connectivity, the smart grid gains exceptional efficiency, dependability, and agility, providing a more robust and environmentally friendly energy infrastructure.