To address the high computational and communication overheads and the limited edge security found in many existing batch verification methods for Mobile Edge Computing (MEC), this paper presents a blockchain-based batch authentication and symmetric group key agreement protocol. A core feature of this protocol is the establishment of a shared symmetric key among all authenticated participants. This symmetry in key distribution is fundamental for enabling secure and efficient broadcast or multicast communication within the MEC group. The protocol introduces a chameleon hash function built on elliptic curves, allowing smart mobile devices (SMDs) to generate lightweight signatures. The edge server (ES) then performs efficient large-scale batch authentication using an aggregate signature technique. Considering the need for secure and independent communication between SMDs and ES, the protocol further establishes a one-to-one session key agreement mechanism and uses a Merkle tree to verify session key correctness. Formal verification with ProVerif2.05 tool confirms the protocol’s security and multiple protection properties. Experimental results show that, compared with the CPPBA, ECCAS, and LBVP schemes, the protocol improves computational efficiency of batch authentication by 0.94%, 67.20%, and 49.53%, respectively. For group key agreement, the protocol achieves a 35.26% improvement in computational efficiency over existing schemes.
The rapid growth of urban populations has intensified parking congestion in metropolitan areas, leading to excessive fuel consumption, traffic delays, and environmental concerns. This paper presents the design and implementation of an Internet of Things (IoT)-based smart car parking system using Arduino Uno, NodeMCU (ESP8266), and infrared (IR) sensors. The proposed system detects vacant parking spaces and communicates availability to drivers through the Blynk IoT platform in real time. The system was designed, simulated, and tested using integrated hardware and software components, with results showing reliable detection and efficient data transmission to the user interface. The project demonstrates a scalable, cost-effective solution suitable for commercial and residential applications.
This study suggests DC motor position control, an interesting control that most manufacturers are interested in. A number of applications use it, but autonomous valet parking is the most prominent. A fuzzy logic controller, a type of artificial intelligence controller, is used to implement this control. Prior to being implemented in LabVIEW software and QNET 2.0 DC motor hardware, it was first simulated in the MATLAB/SIMULINK environment. This controller's simulation feature helps get a favorable result without really harming the material. The same simulated outcome was obtained when this controller was experimented with on a QNET 2.0 DC motor using LabVIEW software. This environment is used to visualize the system's response or outcomes using virtual tools. The system responds quickly and effectively receives the order; there is no overshoot, peak time, or settling time.
Because of its frequent use in diverse systems, the PMSM drive must be controlled. Field-oriented control (FOC) based PMSM drive is modeled in the present work to optimize the torque and speed performance of the PMSM. The FOC is based on a dissociated speed and flux control approach, which controls the speed and flux of the PMSM independently. The standard Proportional Integrator Derivative (PID) controller regulates the speed in FOC, which is noted for its increased resilience in linear systems, however in nonlinear ones, the PID controller responds poorly to changes in the system’s variables. In this case, the best solutions are frequently based on optimization techniques that produce the controller’s gains in every period. Optimizing the PID’s behavior in response to the system’s nonlinear behavior. The novel proposed strategy for enhancing the gains of the PID controller by employing a cost function such as Integral Time Absolute Error (ITAE) is based on PID speed regulation and is optimized using the Ant Colony Optimization algorithm (ACO) for FOC. To confirm the strategy’s aims, the suggested method is implemented on Matlab/Simulink. The simulation results demonstrated the efficiency of the intelligent ACO-FOC control, which delivers good performance in terms of stability, rapidity, and torque fluctuations.
Abstract According to Wikipedia "machine learning is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so". Machine learning has many applications such as banking, bioinformatics, adaptive websites, computer networks, economics, linguistics, online advertising, self-driving cars, dynamic pricing, traffic alerts, social media, search engines, speech recognition, online video streaming, IOT, … Machine learning has become a revolutionary modern engineering tool to solve real-world engineering problems. Because of availability of computing power, more and more engineering problems have been reformulated and solved using this data-driven approach. It is essential for engineers to know how to apply machine learning algorithms to their large amount of data that is generated by the sensors. Educational excellence requires exposing students to the current edge of research. To ensure that student projects are along the same trajectory that the industry is moving, educators must continually introduce emerging techniques, practices, and applications into the curriculum. The field of machine learning is growing rapidly. It is essential that the emerging field of machine learning be integrated into the computer science and engineering curricula. This paper is a study of different approaches that are used by different institutions of higher education around the world to integrate machine learning concepts in their computer science and engineering curricula.
