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The ever-increasing demand for energy and power consumption due to population growth, economic expansion, and evolving consumer choices has led to the need for renewable energy sources. Traditional energy sources such as coal, oil, and gas have contributed to global pollution and have adverse effects on human health. As a result, the use of renewable energy for power generation has increased tremendously. One such area of research is solar irradiation prediction, which utilizes Artificial Intelligence and Machine Learning techniques. With the use of real-time predicted data, the digital twins are intended to add value to the organization by identifying and preventing problems, predicting performance, and improving operations. This paper provides an overview of various learning methods used for predicting irradiance and presents a new ensemble solar irradiance forecasting model that combines eight machine learning models to ensure model diversity. The model's most critical factors for predicting irradiance include temperature, cloudiness index, relative humidity, and day of the week. To conduct a comprehensive analysis, the proposed 8-Stacking Regression Cross Validation (8 STR-CV) model was tested using data from three different climatic zones in India. The model's high accuracy scores of 98.8% for Visakhapatnam, 98% for Nagpur, and 97.8% for the mountainous region make it a valuable tool for future prediction in various sectors, including power generation and utilization planning. [ABSTRACT FROM AUTHOR]

There has been an increasing trend of moving computing activities closer to the edge of the network, particularly in smart city applications (e.g., vehicle-to-everything – V2X). Such a paradigm allows the end user’s requests to be handled/processed by nodes at the edge of the network; thus, reducing latency, and preserving privacy of user data/activities. However, there are a number of challenges in such an edge computing ecosystem. Examples include (1) potential inappropriate utilization of resources at the edge nodes, (2) operational challenges in cache management and data integrity due to data migration between edge nodes, particularly when dealing with vehicular mobility in a V2X application, and (3) high energy consumption due to continuous link breakage and subsequent reestablishment of link(s). Therefore in this paper, we design a blockchain-based secure data processing framework for an edge envisioned V2X environment (hereafter referred to as BloCkEd). Specifically, a multi-layered edge-enabled V2X system model for BloCkEd is presented, which includes the formulation of a multi-objective optimization problem. In addition, BloCkEd comprises an optimal container-based data processing scheme, and a blockchain-based data integrity management scheme, designed to minimize link breakage and reducing latency. Using Chandigarh City, India, as the scenario, we implement and evaluate the proposed approach in terms of its latency, energy consumption, and service level agreement compliance. [ABSTRACT FROM AUTHOR]

With the ongoing trend of smart and Internet-connected objects being deployed across a broad range of applications, there is also a corresponding increase in the amount of data movement across different geographical regions. This, in turn, poses a number of challenges with respect to big data storage across multiple locations, including cloud computing platform. For example, the underlying distributed file system has a large number of directories and files in the form of gigantic trees, which are difficult to parse in polynomial time. Moreover, with the exponential increase of big data streams (i.e., unbounded sets of continuous data flows), challenges associated with indexing and membership queries are compounded. The capability to process such significant amount of data with high accuracy can have significant impact on decision-making and formulation of business and risk-related strategies, particularly in our current Industrial Internet of Things environment (IIoT). However, existing storage solutions are deterministic in nature. In other words, they tend to consume considerable memory and CPU time to yield accurate results. This necessitates the design of efficient quality of service-aware IIoT applications that are able to deal with the challenges of data storage and retrieval in the cloud computing environment. In this paper, we present an effective space-effective strategy for massive data storage using bloom filter (BF). Specifically, in the proposed scheme, the standard BF is extended to incorporate fuzzy-enabled folding approach, hereafter referred to as fuzzy folded BF (FFBF). In FFBF, fuzzy operations are used to accommodate the hashed data of one BF into another to reduce storage requirements. Evaluations on UCI ML AReM and Facebook datasets demonstrate the efficacy of FFBF, in terms of dealing with approximately 1.9 times more data as compared to using the standard BF. This is also achieved without affecting the false positive rate and query time. [ABSTRACT FROM AUTHOR]

With the exponential growth of technologies such as IoT, edge computing, and 5G, a tremendous amount of structured and unstructured data is being generated from different applications in the smart citiy environment in recent years. Thus, there is a need to develop sophisticated techniques that can efficiently process such huge volumes of data. One of the important components of smart cities, ITS, has led to many applications, including surveillance, infotainment, real-time traffic monitoring, and so on. However, its security, performance, and availability are major concerns facing the research community. The existing solutions, such as cellular networks, RSUs, and mobile cloud computing, are far from perfect because these are highly dependent on centralized architecture and bear the cost of additional infrastructure deployment. Also, the conventional methods of data processing are not capable of handling dynamic and scalable data efficiently. To mitigate these issues, this article proposes an advanced vehicular communication technique where RSUs are proposed to be replaced by edge computing platforms. Then secure V2V and V2E communication is designed using the Quotient filter, a probabilistic data structure. In summary, a smart security framework for VANETs equipped with edge computing nodes and 5G technology has been designed to enhance the capabilities of communication and computation in the modern smart city environment. It has been experimentally demonstrated that use of edge nodes as an intermediate interface between vehicle and cloud reduces access latency and avoids congestion in the backbone network, which allows quick decisions to be made based on the traffic scenario in the geographical location of the vehicles. The proposed scheme outperforms the conventional vehicular models by providing an energy-efficient secure system with minimum delay. [ABSTRACT FROM AUTHOR]

Over the last few years, we have witnessed an exponential increase in the computing and storage capabilities of smart devices that has led to the popularity of an emerging technology called edge computing. Compared to the traditional cloud-computing- based infrastructure, computing and storage facilities are available near end users in edge computing. Moreover, with the widespread popularity of unmanned aerial vehicles (UAVs), huge amounts of information will be shared between edge devices and UAVs in the coming years. In this scenario, traffic surveillance using UAVs and edge computing devices is expected to become an integral part of the next generation intelligent transportation systems. However, surveillance in ITS requires uninterrupted data sharing, cooperative decision making, and stabilized network formation. Edge computing supports data processing and analysis closer to the deployed machines (i.e., the sources of the data). Instead of simply storing data and missing the opportunity to capitalize on it, edge devices can analyze data to gain insights before acting on them. Transferring data from the vehicle to the edge for real-time analysis can be facilitated by the use of UAVs, which can act as intermediate aerial nodes between the vehicles and edge nodes. However, as the communication between UAVs and edge devices is generally done using an open channel, there is a high risk of information leakage in this environment. Keeping our focus on all these issues, in this article, we propose a data-driven transportation optimization model where cyber-threat detection in smart vehicles is done using a probabilistic data structure (PDS)- based approach. A triple Bloom filter PDS- based scheduling technique for load balancing is initially used to host the real-time data coming from different vehicles, and then to distribute/collect the data to/from edges in a manner that minimizes the computational effort. The results obtained show that the proposed system requires comparatively less computational time and storage for load sharing, authentication, encryption, and decryption of data in the considered edge-computing-based smart transportation framework. [ABSTRACT FROM AUTHOR]

This study aims to enhance the solar energy harvesting capabilities of Unmanned Aerial Vehicles (UAVs), with a focus on integrating solar power to improve overall energy harvesting systems. The proposed method combines two independent renewable systems to extract electricity from the environment. UAV wings equipped with solar panels capture solar energy, employing optimal power point tracking for increased efficiency. Simulation results utilize an ensemble machine learning algorithm, incorporating environmental variables and UAV data to predict solar power output. A comparative analysis involving various machine learning algorithms provides additional insights gleaned from the UAV dataset. [ABSTRACT FROM AUTHOR]