Disasters are crisis circumstances that put human life in jeopardy. During disasters, public communication infrastructure is particularly damaged, obstructing Search And Rescue (SAR) efforts, and it takes significant time and effort to re-establish functioning communication infrastructure. SAR is a critical component of mitigating human and environmental risks in disasters and harsh environments. As a result, there is an urgent need to construct communication networks swiftly to help SAR efforts exchange emergency data. UAV technology has the potential to provide key solutions to mitigate such disaster situations. UAVs can be used to provide an adaptable and reliable emergency communication backbone and to resolve major issues in disasters for SAR operations. In this paper, we evaluate the network performance of UAV-assisted intelligent edge computing to expedite SAR missions and functionality, as this technology can be deployed within a short time and can help to rescue most people during a disaster. We have considered network parameters such as delay, throughput, and traffic sent and received, as well as path loss for the proposed network. It is also demonstrated that with the proposed parameter optimization, network performance improves significantly, eventually leading to far more efficient SAR missions in disasters and harsh environments.

The World Wide Web is experiencing a daily increase in data transmission because of developments in multimedia technologies. Consequently, each user should prioritize preventing illegal access of this data by encrypting it before moving it over the Internet. Numerous color image encryption schemes have been developed to protect data security and privacy, indifferent to the computation cost. However, most of these schemes have high computational complexities. This research proposes a fast color image scrambling and encryption algorithm depending on different chaotic map types and an S-box that relies on a hyperchaotic map principle. The first step involves converting color image values from decimal representation to binary representation in the scrambling stage by changing the location of the bits according to a proposed swapping algorithm. Next, in the second scrambling stage, the same process occurs after returning color image values from binary representation to decimal representation and generating an S-box with the assistance of two types of chaotic map, namely, a 2D Zaslavsky map and a 3D Hénon map. Thus, this S-box is relied upon to swap the locations of the pixels in the color image. The encryption procedure begins with the production of three key matrices using a hybrid technique that employs two low-complexity types of chaotic map, namely, a 1D Logistic map and a 3D Hénon map, followed by an XORed as a lightweight process between each key generated for the three matrices and the corresponding red, green, and blue image channels. According to the findings, the proposed scheme demonstrates the most efficiency in terms of lowering the computational cost and shows its effectiveness against a wide range of cryptographic attacks.

The process of controlling the flow of products and services from a company by encompassing each stage involved in transforming raw materials and parts into finished items, also delivering them to the final consumer is known as Supply Chain Management (SCM). The development of numerous smart city applications including smart grids, smart homes, smart supply chains, and smart healthcare has drawn attention to the Internet of Things (IoT). Nowadays, researchers are considering the smart healthcare system’s role as a Public Emergency Service (PES) to treat patients promptly. A distributed smart fire brigade system receives little attention like PES to save lives and property from catastrophic fire damage. The conventional PES methods are created using a centralized method that needs a lot of processing power and doesn’t offer timely services. The traditional systems developed for managing the supply chain have drawbacks like single-point failure issues, data integrity, transparency, and lack of trust. To alleviate the existing issues, in this paper, a Blockchain and IoT Enable Secure and Transparent Supply Chain Management framework is utilized for PES in the smart city environment. Further, two edge computing servers, like a service controller and an IoT controller are adapted. The local storage is handled by the service and IoT controller. Thus, it enhances the data processing speed of PES requests and PES fulfillment. The service controller utilizes the Optimal Queue Model to manage the PES requests based on the minimum service queue length. The efficiency of the network is improved by fine-tuning the parameters from the Queue model with the aid of a Revised Fitness-based Political Optimizer (RF-PO). The multi-objective constraints like queue length, utilization, actual arrival time, expected arrival time, and end-to-end delay are utilized for the efficient supply chain system. These stimulated results show the feasibility and effectiveness of the supply chain framework.