The increasing reliance on smart plugs and smart meters in modern electricity grids introduces significant security vulnerabilities, as unauthorized access can compromise grid reliability and stability. Traditional access control models are ill-suited for smart grids’ decentralized and dynamic nature. This paper introduces BACS-HP, a novel Blockchain-Based Access Control Model for Smart Grids that enhances security by incorporating privilege levels and peak hour attributes. Privilege levels prioritize access to critical devices during energy constraints, while the peak hour attribute enables adaptive decision-making to optimize energy allocation during periods of high demand. Unlike existing blockchain-based access control solutions, BACS-HP uniquely combines these context-aware attributes to provide fine-grained access control tailored to the specific needs of smart grids. The model leverages blockchain technology to ensure the secure and decentralized storage of access rights and enforces policies via smart contracts, mitigating single points of failure. Empirical results demonstrate that BACS-HP achieves low-latency security rule updates (between 42 ms and 46 ms), rapid access request processing (between 21 ms and 46 ms), and a high acceptance rate (60%) for critical devices during power outages, outperforming standard ABAC implementations in terms of responsiveness and prioritization. BACS-HP contributes to advancing access control mechanisms in smart grids and highlights the potential of blockchain to meet the security and performance demands of modern energy systems.

Natural disasters, particularly earthquakes and floods, can generate substantial waste, presenting challenges for waste management and energy recovery. This study explores the application of machine learning, specifically Artificial Neural Networks (ANN), to optimize Waste-to-Energy (WTE) conversion processes for disaster-generated Municipal Solid Waste (MSW). Using data from Sundernagar City, India, we constructed and evaluated multiple ANN models (collectively named ANNSNAGAR) to estimate High Heating Value, HHV, based on ultimate/elemental analysis of MSW. The models were rigorously assessed using statistical error metrics. Our best-performing network, with a 9-50-1 architecture (ANNMS), achieved a mean absolute percentage error (MAPE) of 1.47%, demonstrating high predictive accuracy.

Recently, all have witnessed a rapid growth of COVID-19 coronavirus worldwide. The calculation of COVID-19 time-series prediction is done using many techniques like compartment models, machine learning models (ML), and deep learning models. Therefore, in this paper, the authors have proposed an ensemble-based neural network model. Neural networks (NN), along with non-linear autoregressive (NAR) functions, fitting neural networks (FITNET), or fuzzy Systems, are widely used in time-series forecasting. The responses of NAR, FITNET predictor modules are aggregated using fuzzy logic, which improves the final prediction by intelligently integrating outputs of different modules. The whole model was put to the test in terms of forecasting the coronavirus time series in India, at 13 States. In the validation data set, results of ensemble NN models with fuzzy response integration demonstrate extremely better-predicted values. Overall, the results reveal that a modular neural network with fuzzy (MNNF) beats all other approaches in performance metrics, like Root Mean Squared Error (RMSE). Prediction errors of ensemble NN i.e., MNNF are much smaller than those of classic monolithic neural networks, showing advantages of the method proposed. The model provides the prediction for the upcoming 8 days.

Natural Disaster provide serious problems for communities all over the world, with severe effects on the environment, infrastructure, and human lives. Effective disaster management solutions are increasingly important as the frequency and severity of these events rise to limit losses and ensure quick responses. A creative and promising approach to improving disaster management skills is the introduction of the Internet of Things (IoT). A brief description of the use of IoT in managing natural disasters is provided in this abstract. With the help of several networked sensors and devices, the Internet of Things (IoT) enables real-time data gathering, processing, and transmission. Utilizing this technology can result in preemptive and more efficient disaster response plans. Although there is great promise for IoT integration in disaster management, there are also issues with privacy of data, protection, and interoperability. For Internet of Things (IoT) technologies in disaster management to be successfully implemented and accepted, these problems must be addressed. This offers a brief glimpse of the revolutionary potential of IoT in tackling the intricate problems of catastrophe management.

People have been using social media as a category for exchanging content for decades. It has revolutionized communication and enhanced the sharing of information during emergency situations. The key features of social media are collective action, connectivity, comprehensiveness, and clarity. Consequently, it performed a significant function in natural disaster management by keeping track of and reporting disaster-related incidents. The volume and diversity of the data acquired from social media during a natural disaster pose the greatest challenge. For natural disaster management it is extremely difficult to derive actionable information from the data collected from social platforms. Various strategies have been presented in the literature to address the difficulties posed by social media for natural disaster management. The proposed work is a semi-supervised machine learning model for detecting and classifying tweets. The proposed work centre’s on preparing the data by performing cleansing and various transformations, generating word embedding vectors with DistillBERT, using the vision transformer, the image model was constructed. The attention mechanism is utilized for text and image model integration. In the proposed work the data pertaining to seven distinct natural disasters, such as cyclones, floods, and earthquakes are analyzed. A novel decision diffusion technique is proposed for classifying them into informative and non-informative groups and evaluating the accuracy of the results. The MAM model increases the accuracy to 97% for crisisMMD dataset.

Natural disasters cause immense hardship for communities around the world, necessitating effective and coordinated responses to mitigate the consequences on infrastructure and human life. Cloud computing is a breakthrough technology that has enormous promise for improving disaster management tactics. This study investigates the critical role of cloud computing in disaster management, showing its multiple benefits during the phases of planning, response, and recovery. The study investigates how cloud computing improves disaster preparedness by allowing stakeholders to simulate and prepare for various disaster scenarios using data-gathering capabilities, collaborative tools, and simulation models. The paper discusses the role of cloud computing in preparation of disaster management and how an organization can use the latest technology to minimize the impact of disaster on it.

Rapid progress in technology along with globalization have established new requirements for the skills that engineers should demonstrate in the workplace. The traditional academic program and the teaching-learning methods are outdated and do not equip students with important skills such as critical thinking, teamwork, lifelong learning, problem-solving, leadership, and entrepreneurship. In professional life, engineers work in heterogeneous and interdisciplinary teams. This work reports the results of engineering design courses provided at several Saudi engineering faculties. The course is a capstone oriented where interdisciplinary teams work to design a final product using the problem-solving process: Problem definition, solution generation, course of action decision, solution implementation, and solution evaluation.

This paper presents the implementation of an intelligent home-based elderly patient monitoring system. Four patient's physiological parameters are being continuously monitored, namely, temperature, glucose, and 3D accelerometer and gyroscope data for fall detection. Contextual sensors are mounted across the home to observe the patient's surrounding environment such as temperature and humidity. All sensors, wearable and contextual, transmit their measured data to smart gateways (fog layer) via nRF communication protocol. At the fog layer, diverse functions are being carried out, from collected measurements transfer to health care providers for further processing and analysis via Internet (cloud layer), sending push notifications and reports to patient's mobile phone, to alerting ambulance or civil defence authorities in case of an emergency. To insure power autonomy and eliminate the need for frequent sensor node battery replacement, an efficient thermal energy harvesting system is developed. Associated with a boost converter, the thermal energy harvesting system is able to sustain 3.3 OCV leveraging a temperature difference of 20°C between patient's body and room temperature, while achieving an efficiency of 82.6%.

This paper proposes hardware architecture for a fuel estimation algorithm suitable for SoC FPGA. The architecture utilizes 32-point single precision floating point representation. A resource constraint scheduling algorithm is elaborated to synthesize an area efficient architecture. The floating point arithmetic is implemented using commercial IP. Synthesis results for the Virtex-6 FPGA family reveal that the proposed architecture consumes 1655 slices, has a latency of 71 ns and consumes 0.15 µJ. Additionally, the present work describes a software implementation of the fuel estimation algorithm using Zynq-7000 SoC.

Cipher-based message authentication code, CMAC, is a NIST approved standard for checking message integrity and authentication. This work presents a low-latency AES architecture for CMAC. The architecture uses intensive parallel processing per round and takes advantage of the BRAM present in modern FPGA. Experimental results show that for typical IoT application, the proposed architecture has a latency of 10 clock cycles, consumes 1355 slices, 2 BRAMs and achieves a throughput of 3.8Gbps.

This paper proposes an electronic system to combat drifting and traffic noises in the urban area of Saudi Arabia. The proposed solution can be integrated into a smart city platform. The system comprises a sound processing hardware, a CCTV camera, and a GPRS module for wireless IP access. An algorithm to address drifting for noise and accidents is derived and tested over a range of audible traffic noises in Sakakah town. Hardware implementation of the algorithm using Radix-8, 64-point FFT algorithm, and a semiconductor intellectual property is elaborated. The results show that the algorithm produces no false alarm.

This paper elaborates a macroscopic, non-iterative algorithm to estimate the fuel consumption of vehicles. The algorithm uses the Willan's internal combustion engine model and needs no instantaneous values of speed and acceleration. The efficiency of the proposed algorithm has been compared with measurement results for the following three cycles: motor vehicle expert group (MVEG-95), European driving cycle (ECE), and extra-urban driving cycle (EUDC). For eco-routing, experiments show that there are tradeoffs between fuel savings and travel time. Results reported in this paper show that up to 33 % of fuel savings have been achieved at the expense of 3 % increase in trip-time. Finally, for manual gearing, the paper reports that proper gear shifting strategies can have substantial fuel savings. The reported experiments show that shifting from the 3rd to the 4th gear can save fuel consumption by 19%. These savings can reach up to 25% when shifting from the 4th to the 5th gear.

This paper proposes a client-server network architecture for safe pilgrimage in the kingdom of Saudi Arabia. The proposed architecture allows for continuous tracking and trip planning of shuttle-bus during its journey to the holy city of Makkah using assisted GPS. For personnel identification the architecture supports RFID technology. The proposed RFID technology is Java contactless card that enables both bus drivers and travel agencies to prevent unauthorized passengers to board the bus and to supervise authorized passengers during breaks. Furthermore Java card based system facilitates offline and online passengers identifications at the various checking points by the Saudi authorities. Wireless access to the internet is a vital layer in the proposed architecture as it allows for tracking, trip planning and on-line identity verification. Experimental results for wireless access to the internet show that HSPA is better than WiMAX as it offers acceptable quality of service at vehicle speed that exceeds 120kmph.

This paper describes LP-DSM, which is an algorithm used for efficient library characterization in high-level power estimation. LP-DSM characterizes the power consumption of building blocks using the entropy of primary inputs and primary outputs. The experimental results showed that over a wide range of benchmark circuits implemented using full custom design in 0.35-/spl mu/m 3.3 V CMOS process the statistical performance (mean and maximum error) of LP-DSM is comparable or sometimes better than most of the published algorithms. Moreover, it was found that LP-DSM has the lowest prediction sum of squares, which makes it an efficient tool for power prediction. Furthermore, the complexity of the LP-DSM is linear in relation to the number of primary inputs (O(NI)), whereas state of the art published library characterization algorithms have a complexity of O(NI/sup 2/).

Many of the signal and image processing problems concern the suppression of the noise which is non-Gaussian and non-additive. Stack filters are nonlinear filters which are often successfully used for this kind of noise cancellation. A fault detection method is proposed for stack filters. The core of the method is the sample selection probability vectors of stack filters. A simple implementation for fault diagnosis is derived based on this notion.