The standardization of very-large-scale integration (VLSI) physical architecture for VLSI chips and multichip platforms is now in its early stages of development. The purpose of VLSI partitioning is to divide the circuit into numerous smaller circuits with few connections in between. Partitioning is the fundamental problem in circuit design and division. The efficient method of evolutionary computation may be used to tackle the partitioning problem in VLSI circuit design. It provides a heuristic approach to solve this problem by exploring the solution space and incrementally improving the quality of the solutions. In order to obtain the shortest wire length (WL), area, and connections, an evolutionary optimized simulated annealing memetic algorithm (OSAMA) that incorporates one or more local search phases inside its evolutionary cycle was developed.

The ability to interpret and create code is known as cryptography and has been used to exchange information between peer parties securely. An encryption algorithm is a type of network security model that consists of designing and putting into practice cryptographic algorithms and the supporting framework to help secure data. This cryptosystem is intended for use with cloud data. Cloud security, usually called cloud computing security, is the technique of securing infrastructure, applications, and data stored in the cloud from cyber threats and attacks. This study proposes a new encryption method based on the (x; y) points generated by circles to protect data in the cloud. Using a map table, a new mapping technique is shown in this study to transfer a plain text value to a point on a predetermined circle over the finite field GF (pm). This mapping technique produces a high dispersion of various spots for recurrent intensity values while being highly quick, simple to use, and with low entropy for plain text input. The implementation of the encryption and decryption processes is extensively studied and analyzed. Security analysis is done after encryption is implemented to determine how resilient the suggested technique is against statistical threats. The results shown in this study prove the superiority of the proposed algorithm. Received: 12 March 2024 | Revised: 24 May 2024 | Accepted: 31 May 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Smitha Sasi: Conceptualization, Methodology, Software, Validation, Investigation, Data curation, Writing – original draft, Visualization. Srividya Bharadwaj Venkata Subbu: Conceptualization, Methodology, Validation, Investigation, Data curation, Writing – original draft, Visualization. Premkumar Manoharan: Software, Validation, Formal analysis, Resources, Writing – review & editing, Visualization, Supervision, Project administration. Anju Vijayakumar Kulkarni: Formal analysis, Resources, Writing – review & editing, Supervision. Laith Abualigah: Formal analysis, Resources, Writing – review & editing.

The chapter discusses the utilization of advanced methodologies like Quantum Computing and AI in optimizing Arduino programming in IoT devices. The increasing popularity of IoT devices necessitates a growing need to enhance their performance and efficiency. It provides an in-depth explanation of the fundamental principles of Arduino programming and the challenges associated with IoT system programming.It then introduces Quantum Computing concepts, showing how quantum algorithms may resolve complex computational problems more efficiently than classical methods. In this respect, the chapter further goes into AI techniques, including machine learning and neural networks, to provide enhanced decision-making and prediction capabilities in IoT ecosystems. This chapter proposes a framework combining Quantum Computing and AI to optimize performance in IoT devices, demonstrating practical examples and combining them with Arduino-based systems.

The need for cloud storage is growing daily, despite its apparent reliability and scalability. Because of the complexity of data security, including privacy and confidentiality, it is difficult to store and manage user data remotely. An untrustworthy insider could be the cloud third party responsible for managing the data security issues occurring within the cloud services. An automated hybrid cryptographic algorithm for cloud data storage is provided by the current research to improve data protection and guarantee information confidentiality without any disruption from cloud third parties. Two encryption methods that are employed to attain high performance and efficiency are enhanced automated concurrent encryption for data blocks (CEDB) and improved automated asynchronous encryption for data streams (AEDS). By converting the fixed S-box in the traditional AES to a modifying S-box using a ternary Galois field (GF) and complementary ternary GF, we present a novel encryption technique in enhanced automated concurrent encryption. Furthermore, the matrix utilized for the AES mix column approach is converted into a dynamic matrix using the ternary GF and complementary ternary GF. This cryptographic approach aims at providing cloud data security, ensuring confidentiality, and integrity for the user’s information even in compromised situations.