An electric grid consists of transformers, generation centers, communication links, control stations, and distributors. Collectively these components help in moving power from one electricity station to commercial and domestic consumers. Traditional grid stations can’t predict the dynamic need of consumers’ electricity. Furthermore, these traditional grids are not sufficiently strong and adaptable. This is the driving force for the transition towards a smart grid. A modern smart grid is a self-healing, long-lasting electrical system that can adapt to changing client needs. Machine learning has aided in grid stability calculation in the face of dynamically shifting consumer demands. By avoiding a breakdown, the smart grid has been transformed into a reliable smart grid. The authors of this study used a variety of machine learning-based algorithms to estimate grid stability to avoid a breakdown situation. An open-access dataset lying on Kaggle repository has been used for experimental work. Experiments are conducted in a simulation environment generated through Python. Using the Bagging classifier algorithm, the suggested model has attained an accuracy level of 97.9% while predicting the load. A precise prediction of power demand will aid in the avoidance of grid failure, hence improving grid stability and robustness.
Abstract The Internet of Things (IoT) are standard inter connected devices aimed at join everyday object to the internet. This ecosystem include manufacturing, agriculture, smart cities, industry, as well as healthcare. The capacity of controlling and monitoring the objects of the physical world using IoT generate numerous opportunities. However some extra cost is also added to make the device globally accessible. The aggressive growth of the IoT devices, the multifariousness of IoT network technology, and the diversity of IoT use cases generate a question mark regarding the sustainability of the IoT. The aim of the proposed work to contribute in this regard, for that a dynamic integration of IoT objects that is pre determined for (i) creating an IoT platform dynamically (ii) monitoring the current status of IoT environment (iii) measuring the quality of the overall system (iv) helping to utilize all the interconnected efficiently by adding M2M communication, is introduced. Some property set which is suitable for decentralized IoT platform is also explained. Such types of dynamic IoT platform helps in every IoT application domain including industrial IoT. With the propound research, the aim is to create a dynamic IoT platform to simplify the production of next generation. The primary contribution of the proposed paper is a concept which can help to design IoT device in a faster way, which can be called rapid hardware development approach. Efficiently used human resources, that means any one having common technical knowledge can design the device, and reduce the hardware heterogeneous architecture.
Wireless Sensor Networks (WSNs) have been around for over a decade and have been used in many important applications. Energy and reliability are two of the major problems with these kinds of applications. Reliable data delivery is an important issue in WSNs because it is a key part of how well data are sent. At the same time, energy consumption in battery-based sensors is another challenge. Therefore, efficient clustering and routing are techniques that can be used to save sensors energy and guarantee reliable message delivery. With this in mind, this paper develops an energy-efficient and reliable clustering protocol (ERCP) for WSNs. First, an efficient clustering technique is proposed for sensor nodes' energy savings considering different clustering parameters, including the link quality metric, the energy, the distance to neighbors, the distance to the sink node, and the cluster load metric. The proposed routing protocol works based on the concept of a reliable inter-cluster routing technique that saves energy. The routing decisions are made based on different parameters, such as the energy balance metric, the distance to the sink node, and the wireless link quality. Many experiments and analyses are examined to determine how well the ERCP performs. The experiment results showed that the ECRP protocol performs much better than some of the recent algorithms in both homogeneous and heterogeneous networks.
Internet of things (IoT) field has emerged due to the rapid growth of artificial intelligence and communication technologies. The use of IoT technology in modern healthcare environments is convenient for doctors and patients as it can be used in real-time monitoring of patients, proper administration of patient information, and healthcare management. However, the usage of IoT in the healthcare domain will become a nightmare if patient information is not securely maintained while transferring over an insecure network or storing at the administrator end. In this manuscript, the authors have developed a secure IoT healthcare monitoring system using the Blockchain-based XOR Elliptic Curve Cryptography (BC-XORECC) technique to avoid various vulnerable attacks. Initially, the work has established an authentication process for patient details by generating tokens, keys, and tags using Length Ceaser Cipher-based Pearson Hashing Algorithm (LCC-PHA), Elliptic Curve Cryptography (ECC), and Fishers Yates Shuffled Based Adelson-Velskii and Landis (FYS-AVL) tree. The authentications prevent unauthorized users from accessing or misuse the data. After that, a secure data transfer is performed using BC-XORECC, which acts faster by maintaining high data privacy and blocking the path for the attackers. Finally, the Linear Spline Kernel-Based Recurrent Neural Network (LSK-RNN) classification monitors the patient's health status. The whole developed framework brings out a secure data transfer without data loss or data breaches and remains efficient for health care monitoring via IoT. Experimental analysis shows that the proposed framework achieves a faster encryption and decryption time, classifies the patient's health status with an accuracy of 89%, and remains robust compared with the existing state-of-the-art method.
The significance of community structure in complex networks, such as social, biological, and online networks, has been widely recognized. Detecting communities in social media networks typically relies on two sources of information: the network’s topological structure and node attributes. Incorporating rich node content attribute information poses both flexibility and challenges for community detection. Traditional approaches either focus on mining one information source or linearly combining results from both sources, which fails to effectively fuse the information. This paper introduces a practical collaborative learning approach that explores the multi-dimensional attribute characteristics of nodes to facilitate community division. By leveraging graphical matrix decomposition, the proposed algorithm, CDGMF, improves the effectiveness and robustness of community detection. Experimental results demonstrate the method’s ability to effectively utilize node attribute information for guiding community detection, resulting in higher-quality community divisions.
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