A numerical study is presented for the thermo-free convection inside a cavity with vertical corrugated walls consisting of a solid part of fixed thickness, a part of porous media filled with a nanofluid, and a third part filled with a nanofluid. Alumina nanoparticle water-based nanofluid is used as a working fluid. The cavity’s wavy vertical surfaces are subjected to various temperature values, hot to the left and cold to the right. In order to generate a free-convective flow, the horizontal walls are kept adiabatic. For the porous medium, the Local Thermal Non-Equilibrium (LTNE) model is used. The method of solving the problem’s governing equations is the Galerkin weighted residual finite elements method. The results report the impact of the active parameters on the thermo-free convective flow and heat transfer features. The obtained results show that the high Darcy number and the porous media’s low modified thermal conductivity ratio have important roles for the local thermal non-equilibrium effects. The heat transfer rates through the nanofluid and solid phases are found to be better for high values of the undulation amplitude, the Darcy number, and the volume fraction of the nanofluid, while a limit in the increase of heat transfer rate through the solid phase with the modified thermal ratio is found, particularly for high values of porosity. Furthermore, as the porosity rises, the nanofluid and solid phases’ heat transfer rates decline for low Darcy numbers and increase for high Darcy numbers.

The Internet of Drone Things (IoDT) is a trending research area where drones are used to gather information from ground networks. In order to overcome the drawbacks of the Internet of Vehicles (IoV), such as congestion issues, security issues, and energy consumption, drones were introduced into the IoV, which is termed drone-assisted IoV. Due to the unique characteristics of the IoV, such as dynamic mobility and unsystematic traffic patterns, the performance of the network is reduced in terms of delay, energy consumption, and overhead. Additionally, there is the possibility of the existence of various attackers that disturb the traffic pattern. In order to overcome this drawback, the drone-assisted IoV was developed. In this paper, the bio-inspired dynamic trust and congestion-aware zone-based secured Internet of Drone Things (BDTC-SIoDT) is developed, and it is mainly divided into three sections. These sections are dynamic trust estimation, congestion-aware community construction, and hybrid optimization. Initially, through the dynamic trust estimation process, triple-layer trust establishment is performed, which helps to protect the network from all kinds of threats. Secondly, a congestion-aware community is created to predict congestion and to avoid it. Finally, hybrid optimization is performed with the combination of ant colony optimization (ACO) and gray wolf optimization (GWO). Through this hybrid optimization technique, overhead occurs during the initial stage of transmission, and the time taken by vehicles to leave and join the cluster is reduced. The experimentation is performed using various threats, such as flooding attack, insider attack, wormhole attack, and position falsification attack. To analyze the performance, the parameters that are considered are energy efficiency, packet delivery ratio, routing overhead, end-to-end delay, packet loss, and throughput. The outcome of the proposed BDTC-SIoDT is compared with earlier research works, such as LAKA-IOD, NCAS-IOD, and TPDA-IOV. The proposed BDTC-SIoDT achieves high performance when compared with earlier research works.

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As the amount of medical images transmitted over networks and kept on online servers continues to rise, the need to protect those images digitally is becoming increasingly important. However, due to the massive amounts of multimedia and medical pictures being exchanged, low computational complexity techniques have been developed. Most commonly used algorithms offer very little security and require a great deal of communication, all of which add to the high processing costs associated with using them. First, a deep learning classifier is used to classify records according to the degree of concealment they require. Medical images that aren't needed can be saved by using this method, which cuts down on security costs. Encryption is one of the most effective methods for protecting medical images after this step. Confusion and dispersion are two fundamental encryption processes. A new encryption algorithm for very sensitive data is developed in this study. Picture splitting with image blocks is now developed by using Zigzag patterns, rotation of the image blocks, and random permutation for scrambling the blocks. After that, this research suggests a Region of Interest (ROI) technique based on selective picture encryption. For the first step, we use an active contour picture segmentation to separate the ROI from the Region of Background (ROB).

In vehicular ad hoc networks (VANETs), due to the fast-moving mobile nodes, the topology changes frequently. This dynamically changing topology produces congestion and instability. To overcome this issue, privacy-preserving optimization-based cluster head selection (P2O-ACH) is proposed. One of the major drawbacks analyzed in the earlier cluster-based VANETs is that it creates a maximum number of clusters for communication that leads to an increase in energy consumption which reflects in a degradation of the performance. In this paper, enhanced rider optimization algorithm (ROA)-based CH selection is performed and that optimally selects the CH so that effective clusters are created. By analyzing this, the behavior of the bypass rider’s CH is chosen, and this forms the optimized clusters, and during the process of transmission, privacy-preserving mobility patterns are used to secure the network from all kinds of malfunctions which are performed by the new vehicle blending and migration process. The proposed P2O-ACH is simulated using NS-2, and for performance analysis, two scenarios are taken, which contain a varying number of vehicles and varying speeds. For a varying number of vehicles and speeds, the considered parameters are energy efficiency, energy consumption, network lifetime, packet delivery ratio, packet loss, network latency, network throughput, and routing overhead. From the results, it is understood that the proposed method performed better when compared with earlier work, such as GWO-CH, ACO-SCRS, and QMM-VANET.