AI-enabled IoT-Edge Data Analytics for Connected Living

In the digital era, data is one of the most important assets since it conceals valuable information. Developers of data-intensive systems have new challenges at each level of streaming, storing, and processing large quantities of data in a variety of forms and speeds. Obtaining useful information at the proper time and place is also crucial. Since the value of information is inversely proportional to time, real-time data processing and analytics are receiving more attention. Due to the importance of real-time data processing and analytics, this study focuses on real-time data processing concepts and terminology, popular technologies used in real-time data processing and analytics, popular NoSQL storage technologies used in real-time data processing, and real-time data processing application areas. The purpose of this paper is to provide researchers of real-time analysis and developers of data-intensive systems with a comparative perspective on real-time data processing by highlighting the key characteristics of real-time data processing technologies, NoSQL storage technologies, their application domains, and selected examples from previous studies.

Electrical submersible pump (ESP) operation, like many other segments of the oil industry, is recovering from having been drowned in large number of data, the aftermath of information technology revolution, and refocusing on effective utilization of reservoir and production data. This paper presents results of the application of real-time data processing and interpretation in a mature field (Permian basin) through operating and service company alliances. The process involves application of production system efficiency management coupled with newly developed advanced ESP technology and analysis tools-computer simulation software packages that could simulate actual ESP and downhole performance. Data are acquired from the wellsite using data acquisition panels and are sent over a secure network connection to a data hub. The data are also sent for intelligent real-time ESP data processing and interpretation. Well and reservoir monitoring, surveillance, analysis, and diagnosis are also performed. Active collaboration with producing companies at every stage of the process was encouraged through the formation of a new alliance. Finally, various recommendations and suggestions that could operate, optimize the ESP system, reduce well downtime, and prevent catastrophic and premature ESP failures were made. This new approach to ESP operation reduces daily challenges in operating brownfields, such as keeping wells economically viable; solving production problems associated with operating mature assets in today's oil industry that is plagued with continuous declining of manpower resources. As might be expected, lease operating expense (LOE) and/or deferred oil production is reduced. Operational case studies and individual benefits are highlighted and included in this paper. This new approach is a significant departure from the conventional ways of operating ESP where service companies traditionally supply ESPs to producing companies to operate. This new alliance will have a decided influence on future ESP operation and the survival of many brownfields worldwide. Introduction ESP applications as a form of artificial lift method have continued to increase through the years, and significant growth is expected in the next decades as artificial lift requirements increase to a greater extent than current usage. ESP application covers all aspects of hydrocarbon exploitation. Its usage ranges from mature marginal onshore wells to high profile, deep-water subsea wells.

Real-time big data processing is a crucial need of meteorology today. As a matter of fact, the weather is a determining factor in decision making in different areas such as air or sea transport, so acquiring knowledge of the environment in real time is a consequential question. A promising approach for big data treatment in real time is based on the concept of complex events processing CEP). The CEP aims to identify significant events in a cloud of simple and individually detectable events in real time. The meteorological variables, constituting simple event's spaces, offer significant flow of data to be processed using CEP technologies. This real-time data processing is done by phase of their integration with weather systems. This paper discusses the big data's meteorological aspects, and shows the architectural model for integration of weather data and its processing in real time.

In light of the prevailing approach in which data from side-scan sonar (SSS) from Autonomous Underwater Vehicles (AUVs) are primarily processed and visualized post mission, failing to meet the requirements in terms of timeliness for on-the-fly image acquisition, this paper introduces a novel method for real-time processing and superior imaging of navigation strip data from SSS aboard AUVs. Initially, a comprehensive description of the real-time processing sequence is provided, encompassing the integration of multi-source navigation data using Kalman filtering, and high-pass filtering of attitude and heading data to exclude anomalies, as well as the use of bidirectional filtering techniques within and between pings, ensuring real-time quality control of raw data. In addition, this study adopts the semantic segmentation Unet network for automatic real-time tracking of seafloor lines, devises a real-time correction strategy for radial distortion based on historical echo data, and utilizes the alternating direction multiplier method for real-time noise reduction in strip images. With the combined application of these four pivotal techniques, we adeptly address the primary challenges in real-time navigation data processing. In conclusion, marine tests conducted in Bohai Bay substantiate the efficacy of the methodologies delineated in this research, offering a fresh paradigm for real-time processing and superior visualization of SSS navigation strip data on AUVs.

The ability to interpret spatiotemporal data streams in real-time is critical for a range of systems. However, processing vast amounts of spatiotemporal data out of several sources, such as online traffic, social platforms, sensor networks, and other sources, is a considerable challenge. The major goal of this study is to create a framework for processing and analyzing spatiotemporal data from multiple sources with irregular shapes so that researchers can focus on data analysis instead of worrying about the data sources' structure. We introduced a novel spatiotemporal data paradigm for true-real-time stream processing, which enables high-speed and low-latency real-time data processing, with these considerations in mind. A comparison of two state-of-the-art real-time process architectures was offered, as well as a full review of the various open-source technologies for real-time data stream processing, and their system topologies were also presented. Hence, this study proposed a brand-new framework that integrates Apache Kafka for spatiotemporal data ingestion, Apache flink for true real-time processing of spatiotemporal stream data, as well as machine learning for real-time predictions, and Apache Cassandra at the storage layer for distributed storage in real-time. The proposed framework was compared with others from the literature using the following features: Scalability (Sc), prediction tools (PT), data analytics (DA), multiple event types (MET), data storage (DS), Real-time (Rt), and performance evaluation (PE) stream processing (SP), and our proposed framework provided the ability to handle all of this task.

This exploration aims to transfer, process and store multimedia information timely, accurately and comprehensively through computer comprehensive technology processing, and organically combine various elements under the background of big data analysis, so as to form a complete intelligent platform design for multimedia information processing and application. In this exploration, the intelligent vehicle monitoring system is taken as an example. Data acquisition, data transmission, real-time data processing, data storage and data application are realized through the real-time data stream processing framework of [Formula: see text] of big data technology. Data interaction is realized through Spring, Spring MVC, VUE front-end framework, and Ajax asynchronous communication local update technology. Data storage is achieved through Red is cache database, and intelligent vehicle operation supervision system is achieved through multimedia information technology processing. Its purpose is to manage the vehicle information, real-time monitor the running state of the vehicle and give an alarm when there are some problems. The basic functions of vehicle operation monitoring and management system based on big data analysis are realized. The research on the design of vehicle operation monitoring and management system based on big data analysis shows that big data technology can be applied to the design of computer multimedia intelligent platform, and provides a reference case for the development of computer multimedia intelligent platform based on big data analysis.

Purpose: The study sought to explore the impact of edge computing on real-time data processing. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the impact of edge computing on real-time data processing. Preliminary empirical review reveled that edge computing significantly reduced latency and enhanced efficiency in real-time data processing across various industries by bringing computational resources closer to data sources. It highlighted the technology's ability to handle large volumes of IoT-generated data, improve security by localizing data processing, and drive innovation and economic growth through new applications and services. Edge computing's decentralized approach proved essential for reliable and robust data handling, particularly in critical sectors like healthcare and finance, ultimately solidifying its importance in the digital transformation landscape. Unique Contribution to Theory, Practice and Policy: The Diffusion of Innovations Theory, Resource-Based View (RBV) and Sociotechnical Systems Theory may be used to anchor future studies on edge computing on real-time data processing. The study recommended expanding theoretical frameworks to include the unique aspects of edge computing, investing in robust edge infrastructure, and developing standardized protocols and best practices. It emphasized the need for government incentives and supportive regulatory frameworks to promote adoption, and suggested that academic institutions incorporate edge computing into curricula. Additionally, the study called for ongoing research to address emerging challenges and opportunities, ensuring continuous advancement and effective implementation of edge computing technologies.

Recent advances in BazookaSPECT: Real-time data processing and the development of a gamma-ray microscope

Recent advances in mobile technologies have enabled a plethora of new applications. The hardware capabilities of mobile devices, however, are still insufficient for real-time stream data processing (e.g., real-time video stream). In order to process real-time streaming data, most existing applications offload the data and computation to a remote cloud service, such as Apache Storm or Apache Spark Streaming. Offloading streaming data, however, has high costs for users, e.g., significant service fees and battery consumption. To address these challenges, we design, implement and evaluate Mobile Storm, the first stream processing platform for mobile clouds, leveraging clusters of local mobile devices to process real-time stream data. In Mobile Storm, we model the workflow of a real-time stream processing job and decompose it into several tasks so that the job can be executed concurrently and in a distributed manner on multiple mobile devices. Mobile Storm was implemented on Android phones and evaluated extensively through a real-time HD video processing application. The result shows that Mobile Storm effectively processes HD Video Stream in a mobile cloud, which would be impossible on a single mobile device.

Major advances in telecommunications and the Internet of Things have given rise to numerous smart city scenarios in which smart services are provided. What was once a dream for the future has now become reality. However, the need to provide these smart services quickly, efficiently, in an interoperable manner and in real time is a cutting-edge technological challenge. Although some software architectures offer solutions in this area, these are often limited in terms of reusability and maintenance by independent modules —involving the need for system downtime when maintaining or evolving, as well as by a lack of standards in terms of the interoperability of their interface. In this paper, we propose a fully reusable microservice architecture, standardized through the use of the Web of things paradigm, and with high efficiency in real-time data processing, supported by complex event processing techniques. To illustrate the proposal, we present a fully reusable implementation of the microservices necessary for the deployment of the architecture in the field of air quality monitoring and alerting in smart ports. The performance evaluation of this architecture shows excellent results.

The radiation tolerant intelligent memory stack (RTIMS), suitable for both geostationary and low Earth orbit missions, has been developed. The memory module is fully functional and undergoing environmental and radiation characterization. A self-contained "flight-like" module is expected to be completed in 2006. RTIMS provides reconfigurable circuitry and 2 gigabits of error corrected or 1 gigabit of triple redundant digital memory in a small package. RTIMS utilizes circuit stacking of heterogeneous components and radiation shielding technologies. A reprogrammable field programmable gate array (FPGA), six synchronous dynamic random access memories, linear regulator, and the radiation mitigation circuitries are stacked into a module of 42.7mm /spl times/ 42.7mm /spl times/13.00mm. Triple module redundancy, current limiting, configuration scrubbing, and single event function interrupt detection are employed to mitigate radiation effects. The mitigation techniques significantly simplify system design. RTIMS is well suited for deployment in real-time data processing, reconfigurable computing, and memory intensive applications.

The problems of design, computer simulation, manufacture of the device sample and its characteristics investigation of the acoustooptical scanner based on surface acoustic waves for 2D data processing in real time are discussed. The method is based on waveguide Bragg and radiation acoustooptic interactions with a synthesized optical aperture using a few optical beams. The total information field is divided into subareas, each of which is irradiated using an individual optical beam controlled by the corresponding waveguide acoustooptic Bragg cell and interaction cell. During the work elaboration of unit contraction, computer simulation, fabrication sample on based Y-cut lithium niobate and its characteristics investigated. This unit makes it possible to process in real time 2600times650 scanning points (information bits) representing two-dimensional arrays

In order to meet the tomato color sorting system with the requirements of multi-channel data acquisition and real-time processing, [ ]firstly, a multi-channel color digital acquisition and processing technique program based on FPGA were proposed . Then, a multi-channel data acquisition and real-time parallel processing system was built. The RGB color sensors were slected as detection components and the FPGA chip EP1C3T144C8 was slected as a core of data acquisition and processing. Meanwhile, ports of signal acquisition and the solenoid valve control were adopted a two-way analog switch HC4051 for saving I/O ports of FPGA. Thirdly, under the modular design and module reuse principles, we developed all modules with VHDL Language in QuartusII development environment. Finally, RTL and time sequence simulation for the system were done. The results showed that the connections between the modules accord with control logics. [²»´o¶O accorded ¹ýE¥E½] The performance of system time sequencev are good, which can meet with the requirements of real-time processing of tomato color sorting. On these bases, we also conducted a tomato sorting test verification. The results showed that the average accuracy of sorting is 98.70% and coefficient of variation is 0.01. This results can meet requirements of production.

Vector databases are essential for AI-driven cybersecurity tasks, such as intrusion detection, anomaly detection, and threat intelligence retrieval, where high-dimensional security data like network traffic patterns, user behavior analytics, and security event logs are processed. However, the performance of these systems often relies on manual selection and tuning of indexing families (e.g., HNSW, IVF-PQ, ScaNN) and hyperparameters, which is inefficient and impractical in dynamic security environments. In this paper, we propose GenTune-CyberDB, a workload-generative, cross-family auto-tuning framework specifically designed for cybersecurity applications. GenTune-CyberDB leverages workload generation to create realistic attack and anomaly detection queries, optimizing database performance for real-time security data processing. It performs multi-objective, multi-fidelity optimization on index families, execution plans, and hyperparameters, considering constraints like latency, memory, and build time, ultimately improving detection efficiency and resource usage. GenTune-CyberDB demonstrates significant gains in recall and latency optimization, achieving up to 60% memory reduction with minimal recall loss (≤1%). The system adapts to evolving attack patterns and workloads, ensuring robustness even with shifts in data distribution. By automating the tuning process, GenTune-CyberDB offers superior performance for cybersecurity deployments compared to traditional, manually-tuned systems, delivering better recall-latency-memory trade-offs and improving overall security infrastructure.

Applications on smartphones are extremely popular as users can download and install them very easily from a service provider's application repository. Most of the applications are thoroughly tested and verified on a target smartphone platform; however, some applications could be very computationally intensive and overload the smartphone's resource capability. In this paper, we describe a method to predict the total processing time when offloading part of an application from smartphones to nearby servers. In our method, if an application developercan (1) define a basic model of the problem (e. g., f(x)=ax+b) and (2) implement an algorithm to update the model (e. g., least squares method), the application quickly adjusts the parameters of the model and minimizes the difference between predicted and measured performance adaptively. This accurate prediction helps dynamically determine whether or not it is worth offloading tasks and the expected performance improvement. Since this model's simplicity greatly reduces the time required for profiling the performance of the application at run-time, it enables users to start using an application without pre-computing a performance profile. Our experiments show that our update parameter protocol for the performance prediction functions works sufficiently well for a face detection problem. The protocol requires on average 7.8 trials to update prediction parameters, and the prediction error stays less than 10% for the rest of the trials. By offloading the face detection task to a nearby server for an image of 1.2Mbytes, the performance improved from 19 seconds to 4 seconds. This research opens up the possibility of new applications in real-time smartphone data processing by harnessing nearby computational resources.