Developing Priority-Based Control Mechanisms for Grid Ancillary Services through Plug-In Electric Vehicle Charging and Discharging

The increasing integration of renewable energy sources into power grids presents challenges for maintaining grid stability and providing essential ancillary services such as frequency regulation, voltage support, and reserve capacity. Plug-in electric vehicles (PEVs) have emerged as a flexible resource for delivering these services through controlled charging and discharging. However, the efficient coordination of large fleets of PEVs to support the grid while minimizing impacts on user convenience remains a challenge. This paper proposes a novel priority-based control mechanism that optimizes the participation of PEVs in ancillary services based on vehicle state of charge (SOC), trip schedules, grid requirements, and the availability of distributed generation resources. The proposed system assigns priorities to individual PEVs, enabling a dynamic response to grid needs while accounting for user preferences and vehicle readiness. The control strategy is evaluated through simulations that model the interaction between PEVs and the grid under various conditions. Results demonstrate that the priority-based control mechanism significantly improves grid stability and reduces energy costs while ensuring that user mobility requirements are met. These findings suggest that the proposed approach can enhance the role of PEVs in grid management and facilitate the transition to a more resilient and sustainable energy system. Future work will explore the integration of real-time pricing mechanisms and the broader implementation of vehicle-to-grid (V2G) capabilities

Optimized Scheduling of Plug-In Hybrid Electric Vehicles with Distributed Generation: Adapting to Various Vehicle Trip Models

This study presents a novel approach for optimizing the scheduling of plug-in hybrid electric vehicles (PHEVs) integrated with distributed generation systems. As PHEVs gain importance in the transition to sustainable transportation, effective energy management strategies are critical for maximizing their benefits. This research introduces an optimization model that considers various vehicle trip profiles, including daily commuting, long-distance travel, and variable trip frequencies. The model integrates distributed generation sources such as solar and wind energy to enhance charging efficiency and minimize operational costs. The performance of the proposed scheduling strategy was evaluated across different trip scenarios, focusing on key metrics such as energy utilization, cost savings, and emissions reduction using simulations. Results indicate that tailoring PHEV scheduling to specific trip profiles significantly enhances overall system efficiency, particularly when combined with renewable energy sources. This study contributes to the advancement of smart grid applications and highlights the importance of dynamic scheduling in fostering the adoption of PHEVs within sustainable energy systems

Cross Validation Machine Learning Model Predicts More Accurate: A Comparative Study of Heart Disease Using Linear Regression, Support Vector Machine, K Neighbors and Random Forest Models

This primary research paper focuses on the utilization of cross-validation, where each iteration of test data is uniquely structured to ensure optimal model performance by combining weak learners for improved model final accuracy. In the machine learning process, data is typically divided into two types of training/tests of 70% and 30% split, and cross-validation for training and evaluation purposes. This research study involves transforming the original datasets and comparative analysis cross-validation using LR, SVM, KNN, and RF methodologies to heart disease classification datasets. The objective is to easily identify the average accuracy of model predictions and subsequently make recommendations for model selection based on both cross-validated increased (15%) more and non-cross-validated approaches. From the comparing each model accuracy scores it is found that the logistic regression and k-nearest neighbor models achieved the highest accuracy of 81% among the four models. Similarly, the random forest model attained an F1 score of 95%, indicating the highest accuracy score. These findings can be further corroborated using learning curve validation. Conversely, the linear regression model exhibited the lowest accuracy of 84% among the four machine learning models

A Survey on Reversible Image Data Hiding Using the Hierarchical Block Embedding Technique

The use of graphics for data concealment has significantly advanced the fields of secure communication and identity verification. Reversible data hiding (RDH) involves hiding data within host media, such as images, while allowing for the recovery of the original cover. Various RDH approaches have been developed, including difference expansion, interpolation techniques, prediction, and histogram modification. However, these methods were primarily applied to plain photos. This study introduces a novel reversible image transformation technique called Block Hierarchical Substitution (BHS). BHS enhances the quality of encrypted images and enables lossless restoration of the secret image with a low Peak Signal-to-Noise Ratio (PSNR). The cover image is divided into non-overlapping blocks, and the pixel values within each block are encrypted using the modulo function. This ensures that the linear prediction difference in the block remains consistent before and after encryption, enabling independent data extraction without picture decryption. In order to address the challenges associated with secure multimedia data processing, such as data encryption during transmission and storage, this survey investigates the specific issues related to reversible data hiding in encrypted images (RDHEI). Our proposed solution aims to enhance security (low Mean Squared Error) and improve the PSNR value by applying the method to encrypted images

An Improved Reversible Data Hiding with Hierarchical Embedding for Encrypted Images and BBET

This research introduces an enhanced reversible data hiding (RDH) approach incorporating hierarchical embedding for encrypted images and employs a novel technique termed BBET (Best Bits Embedding Technique). RDH involves concealing information within a host sequence, enabling the restoration of both the host sequence and embedded data without loss from the marked sequence. While RDH has traditionally found applications in media annotation and integrity authentication, its utilisation has expanded into diverse fields. Given the rapid advancements in digital communication, computer technologies, and the Internet, ensuring information security poses a formidable challenge in safeguarding valuable data. Various reversible and stenographic techniques exist for covertly embedding or protecting data, spanning text, images, and protocols, and facilitating secure transmission to intended recipients. An influential approach in data security is reversible data hiding in encrypted images (RDHEI). This paper distinguishes between the conventional RDHEI technique, characterised by lower Peak Signal-to-Noise Ratio (PSNR) and higher Mean Squared Error (MSE), and proposes an improved RDHEI technique. As the prevalence of digital techniques for image transmission and storage rises, preserving image confidentiality, integrity, and authenticity becomes paramount. Text associated with an image, such as authentication or author information, can serve as embedded data. The recipient must adeptly recover both the concealed data and the original image. Reversible data-hiding techniques ensure the exact recovery of the original carrier after extracting the encrypted data. Classification of RDHEI techniques is based on the implemented method employed. This paper delves into a comprehensive exploration of techniques applicable to difference expansion, histogram shifting, and compression embedding for reversible data hiding. Emphasis is placed on the necessity for a reversible data-hiding technique that meticulously restores the host image. Furthermore, the study evaluates performance parameters associated with encryption processes, scrutinising their security aspects. The investigation utilises the MATLAB tool to develop the proposed BBET technique, comparing its efficacy in embedding and achieving enhanced security features. The BBET technique is characterised by reliability, high robustness, and secure data hiding, making it a valuable addition to the evolving landscape of reversible data hiding methodologies

An Optimised and Efficient Routing Protocol Application for IoV: A Review

Mobile ad hoc network (MANET) is a wireless network without a centralised administrator, where each node acts as a router forwarding data packets to other nodes. The study compares the performance of three routing protocols (AODV, OLSR, and DSDV) using the NS2 simulator under various mobility models. The proposed work introduces a modified protocol, MAODV, which combines the features of AODV protocols to optimise energy consumption, minimise transmissions, and find an optimum path for data transmission. The proposed method is compared with the standard AODV protocol. It shows better average throughput and packet delivery ratio results in a vehicular ad hoc network (VANET) scenario

Secure Group Communication in Delay Tolerant Mobile Ad-Hoc Network

Delay-tolerant networks (DTNs) are well-known for delivering various types of information from different senders in a multicast manner, both in centralised and decentralised networks. Wireless mobile nodes form small networks in which one or more senders transmit data to one or more destinations through intermediate nodes. DTN routing protocols differ from traditional wireless routing protocols. There are security threats in DTNs, such as blackhole attackers dropping data, jamming attacks consuming bandwidth, and Vampire attacks depleting battery power and available bandwidth. This paper proposes a prevention scheme to detect and mitigate all three types of attackers in multicast communication. These attackers can impact performance by generating false replies, flooding with redundant information, and wasting communication power. The primary focus of this paper is on security issues related to DTN routing protocols. In order to counter malicious nodes, a blacklist is maintained, and if a neighbour identifies a node as malicious, it excludes packets from that node. Meanwhile, the neighbour continues sending packets to the malicious node, except for broadcast packets, which are dropped. If a node is found to forward no packets or only some packets by all its neighbours, any reply it gives to route requests is disregarded, and any request it initiates is ignored. Successful data reception at the destination indicates that hop-based data delivery maintains a record of successful transmissions. The proposed security scheme demonstrates improved performance

Energy and Load Aware Multipath Route Selection for Mobile Ad hoc Networks

Routing protocols are crucial in delivering packets from source to destination in scenarios where destinations are not directly within the sender’s range. Various routing protocols employ different strategies, but their presence is indispensable for seamless data transfer from source to destination. Multipath routing, while offering load balancing, often falls short in efficiently distributing the network’s load, thus adversely impacting the vital communication resource—energy—due to packet loss. This paper introduces an Energy-Efficient Load-Aware Routing (ELAM) scheme to enhance the routing performance of Mobile Ad hoc Networks (MANETs). Our motivation stems from the observation that many multipath routing protocols are designed based on a single criterion, such as the shortest path, often neglecting load balancing or energy conservation. While the Ad Hoc On-Demand Multipath Distance Vector (AOMDV) protocol demonstrates improved performance compared to unipath routing schemes, achieving both load balancing and energy efficiency remains challenging.  The proposed ELAM scheme considers energy conservation, the shortest path, and load balancing to enhance the performance of multipath routing protocols. ELAM considers the shortest path and energy conservation while accommodating more than two paths in a MANET. We introduce an energy factor that contributes to the network’s lifespan, with efficient load balancing enhancing the longevity of nodes and the overall network. The energy factor provides insights into the energy status, and we evaluate the performance of AODV, AOMDV, and the proposed ELAM. The results demonstrate that the proposed scheme outperforms existing protocols and effectively manages unnecessary energy consumption by mobile nodes. Our performance analysis reveals a minimum 5% improvement in throughput and Packet Delivery Ratio (PDR), indicating reduced packet dropping and network delays

Improve Performance Wireless Sensor Network Localization using RSSI and AEMM

Improve wireless sensor network localisation performance using RSSI and an advanced error minimisation method (AEMM). WSNs remain domain-specific and are typically deployed to support a single application. However, as WSN nodes become more powerful, it becomes increasingly important to investigate how multiple applications can share the same WSN infrastructure. Virtualisation is a technology that may allow for this sharing. The issues surrounding wireless sensor node localisation estimation are still being researched. There are a large number of Wireless Sensor Networks (WSNs) with limited computing, sensing, and energy capabilities. Localisation is one of the most important topics in wireless sensor networks (WSNs) because location information is typically useful for many applications. The locations of anchor nodes and the distances between neighbouring nodes are the primary data in a localisation process. The complexity and diversity of current and future wireless detector network operations drive this. Several single schemes have been proposed and studied for position estimation, each with advantages and limitations. Nonetheless, current methods for evaluating the performance of wireless detector networks are heavily focused on a single private or objective evaluation. Accurate position information in a wireless detector network is critical for colourful arising operations (WSN). It is critical to reducing the goods of noisy distance measures to improve localisation accuracy. Existing works (RSSI) are detailed and critically evaluated, with a higher error rate using a set of scenario requirements. Our proposed method (AEMM) is critical for detecting and dealing with outliers in wireless sensor networks to achieve a low localisation error rate. The proposed method (AEMM) for localisation and positioning nodes in wireless sensor networks supported by IOT and discovering the appropriate position of several nodes addresses all of the issues in WSN

Image Enhancement Based on Histogram Equalization with Linear Perception Neural Network Method

Image enhancement poses a formidable challenge in low-level image processing. While various strategies, such as histogram equalisation, multipoint histogram equalisations, and picture element-dependent contrast preservation, have been employed, the efficacy of these approaches has not consistently met expectations. In response, this paper proposes a novel image enhancement method based on a linear perception neural network, demonstrating superior results in contrast improvement with brightness preservation. The proposed method leverages the interdependence of image components through a linear perceptron network, incorporating curvelet transform for image transformation into a multi-resolution mode. This transformative approach identifies component differences in picture elements, establishing a dependency characteristic matrix as a weight vector for the perceptron network. The perceptron network dynamically adjusts the weights of input image values, enhancing contrast while preserving brightness. Extensive testing of the image interdependence linear perception neural network method for contrast improvement has been conducted on multiple images. To quantify brightness preservation, comparative analysis with existing image enhancement strategies, such as histogram equalisation, was performed using Absolute Mean Brightness Error (AMBE) metrics. A smaller AMBE value indicates better preservation, while the Peak signal-to-noise ratio (PSNR) was employed to measure contrast improvement, with higher PSNR values indicating superior results. The proposed method (LPNNM) was rigorously evaluated against the conventional histogram equalisation (HE) technique for image enhancement. The results demonstrated that the LPNNM method outperforms HE in terms of both brightness preservation (as indicated by AMBE) and contrast improvement (as indicated by PSNR). This research contributes a robust and effective solution to the challenge of image enhancement, offering a more advanced alternative to existing methodologies