The gradual transition from a traditional transportation system to an intelligent transportation system (ITS) has paved the way to preserve green environments in metro cities. Moreover, electric vehicles (EVs) seem to be beneficial choices for traveling purposes due to their low charging costs, low energy consumption, and reduced greenhouse gas emission. However, a single failure in an EV’s intrinsic components can worsen travel experiences due to poor charging infrastructure. As a result, we propose a deep learning and blockchain-based EV fault detection framework to identify various types of faults, such as air tire pressure, temperature, and battery faults in vehicles. Furthermore, we employed a 5G wireless network with an interplanetary file system (IPFS) protocol to execute the fault detection data transactions with high scalability and reliability for EVs. Initially, we utilized a convolutional neural network (CNN) and a long-short term memory (LSTM) model to deal with air tire pressure fault, anomaly detection for temperature fault, and battery fault detection for EVs to predict the presence of faulty data, which ensure safer journeys for users. Furthermore, the incorporated IPFS and blockchain network ensure highly secure, cost-efficient, and reliable EV fault detection. Finally, the performance evaluation for EV fault detection has been simulated, considering several performance metrics, such as accuracy, loss, and the state-of-health (SoH) prediction curve for various types of identified faults. The simulation results of EV fault detection have been estimated at an accuracy of 70% for air tire pressure fault, anomaly detection of the temperature fault, and battery fault detection, with R² scores of 0.874 and 0.9375.

The existing grid infrastructure has already begun transforming into the next-generation cyber-physical smart grid (SG) system. This transformation has improved the grid’s reliability and efficiency but has exposed severe vulnerabilities due to growing cyberattacks and threats. For example, malicious actors may be able to tamper with system readings, parameters, and energy prices and penetrate to get direct access to the data. Several works exist to handle the aforementioned issues, but they have not been fully explored. Consequently, this paper proposes an AI-ADP scheme for the SG system, which is an artificial intelligence (AI)-based attack-detection and prevention (ADP) mechanism by using a cryptography-driven recommender system to ensure data security and integrity. The proposed AI-ADP scheme is divided into two phases: (i) attack detection and (ii) attack prevention. We employed the extreme gradient-boosting (XGBoost) mechanism for attack detection and classification. It is a new ensemble learning methodology that offers many advantages over similar methods, including built-in features, etc. Then, SHA-512 is used to secure the communication that employs faster performance, allowing the transmission of more data with the same security level. The performance of the proposed AI-ADP scheme is evaluated based on various parameters, such as attack-detection accuracy, cycles used per byte, and total cycles used. The proposed AI-ADP scheme outperformed the existing approaches and obtained 99.12% accuracy, which is relatively high compared to the pre-existing methods.

The search algorithm based on symbiotic organisms’ interactions is a relatively recent bio-inspired algorithm of the swarm intelligence field for solving numerical optimization problems. It is meant to optimize applications based on the simulation of the symbiotic relationship among the distinct species in the ecosystem. The task scheduling problem is NP complete, which makes it hard to obtain a correct solution, especially for large-scale tasks. This paper proposes a modified symbiotic organisms search-based scheduling algorithm for the efficient mapping of heterogeneous tasks to access cloud resources of different capacities. The significant contribution of this technique is the simplified representation of the algorithm’s mutualism process, which uses equity as a measure of relationship characteristics or efficiency of species in the current ecosystem to move to the next generation. These relational characteristics are achieved by replacing the original mutual vector, which uses an arithmetic mean to measure the mutual characteristics with a geometric mean that enhances the survival advantage of two distinct species. The modified symbiotic organisms search algorithm (G_SOS) aims to minimize the task execution time (makespan), cost, response time, and degree of imbalance, and improve the convergence speed for an optimal solution in an IaaS cloud. The performance of the proposed technique was evaluated using a CloudSim toolkit simulator, and the percentage of improvement of the proposed G_SOS over classical SOS and PSO-SA in terms of makespan minimization ranges between 0.61–20.08% and 1.92–25.68% over a large-scale task that spans between 100 to 1000 Million Instructions (MI). The solutions are found to be better than the existing standard (SOS) technique and PSO.

To enable resource-constrained wireless devices (WDs) to process computation-intensive and latency-sensitive computation tasks, the wireless powered mobile edge computing (WP-MEC) network has been proposed as a promising approach. Incorporating mobile cloud computing in the WP-MEC network, we investigate the wireless powered mobile edge-cloud computing (WP-MECC) network, where WDs first harvest energy from a hybrid access point (HAP), and then consume the harvested energy to compute the tasks locally, offload them to the HAP for computation, or offload them to the cloud server (CS) via the relaying of the HAP. To pursue fairness among WDs, we minimize the maximum task completion time (TCT) of WDs by jointly optimizing time resources, computing mode selection, and computation resources. We prove the minimization problem is NP-hard. To tackle the problem, we decompose it into the sub-problem and top-problem, and propose an alternate optimization-based resource allocation and computing mode selection (ARACM) algorithm with low computational complexity, which achieves a comparable performance with the exhaustive search method in terms of the minimal maximum TCT of WDs. Moreover, we propose a deep reinforcement learning (DRL)-based resource allocation and computing mode selection (DRACM) algorithm with less execution latency than the ARACM algorithm. Numerical results show that the two proposed algorithms achieve satisfactory performance in terms of the minimal maximum TCT of WDs and execution latency.

This study investigates the impact of factors (network coverage, customer service, video calls, and downloading Speed) of 3G and 4G telecommunication services performance on customer satisfaction in the Punjab region of Pakistan. This research indicates how to make strong relations with customers and what factors of the 3G and 4G networks need to be improved to enhance the revenue of telecom operator companies. The study has recognized the four main hypotheses responsible for checking the level of customer satisfaction in the telecommunication industry of Pakistan in the Punjab region. The study depends on essential insights gathered on the arbitrary premise from 300 clients of significant telecom administrators in the Punjab area. The respondents have selected on irregular premises and were welcomed to express their sentiments through an organized survey. A complete questionnaire has been utilized for statistics collection and exists based on the analysis of descriptive measurement, correlation, and regression analysis and analyzed through SmartPLS software. They indicate that the independent variables network coverage, customer service, video calls, and downloading speed are key driving factors of customer satisfaction. Among all independent variables “Internet downloading speed” highly impacts the dependent variable “customer satisfaction” based on 3G and 4G network performance. There are a limited number of studies that focus on customer satisfaction in the telecommunication sector in Pakistan. The study will fill the gap in the literature and help service providers to increase the satisfaction level of their customers and captivate new customers.

The demand for cybersecurity is growing to safeguard information flow and enhance data privacy. This essay suggests a novel authenticated public key elliptic curve based on a deep convolutional neural network (APK-EC-DCNN) for cybersecurity image encryption application. The public key elliptic curve discrete logarithmic problem (EC-DLP) is used for elliptic curve Diffie–Hellman key exchange (EC-DHKE) in order to generate a shared session key, which is used as the chaotic system’s beginning conditions and control parameters. In addition, the authenticity and confidentiality can be archived based on ECC to share the $EC$ parameters between two parties by using the EC-DHKE algorithm. Moreover, the 3D Quantum Chaotic Logistic Map (3D QCLM) has an extremely chaotic behavior of the bifurcation diagram and high Lyapunov exponent, which can be used in high-level security. In addition, in order to achieve the authentication property, the secure hash function uses the output sequence of the DCNN and the output sequence of the 3D QCLM in the proposed authenticated expansion diffusion matrix (AEDM). Finally, partial frequency domain encryption (PFDE) technique is achieved by using the discrete wavelet transform in order to satisfy the robustness and fast encryption process. Simulation results and security analysis demonstrate that the proposed encryption algorithm achieved the performance of the state-of-the-art techniques in terms of quality, security, and robustness against noise- and signal-processing attacks.