Showing papers by "Muhammad Ali Babar published in 2023"
TL;DR: In this article , an optimized IoT-enabled big data analytics architecture for edge-cloud computing using machine learning is proposed, where an edge intelligence module is introduced to process and store the big data efficiently at the edges of the network.
Abstract: The awareness of edge computing is attaining eminence and is largely acknowledged with the rise of the Internet of Things (IoT). Edge-enabled solutions offer efficient computing and control at the network edge to resolve the scalability and latency-related concerns. Though, it comes to be challenging for edge computing to tackle diverse applications of IoT as they produce massive heterogeneous data. The IoT-enabled frameworks for Big Data analytics face numerous challenges in their existing structural design, for instance, the high volume of data storage and processing, data heterogeneity, and processing time among others. Moreover, the existing proposals lack effective parallel data loading and robust mechanisms for handling communication overhead. To address these challenges, we propose an optimized IoT-enabled big data analytics architecture for edge–cloud computing using machine learning. In the proposed scheme, an edge intelligence module is introduced to process and store the big data efficiently at the edges of the network with the integration of cloud technology. The proposed scheme is composed of two layers: 1) IoT–edge and 2) cloud processing. The data injection and storage is carried out with an optimized MapReduce parallel algorithm. An optimized yet another resource negotiator (YARN) is used for efficiently managing the cluster. The proposed data design is experimentally simulated with an authentic data set using Apache Spark. The comparative analysis is decorated with the existing proposals and traditional mechanisms. The results justify the efficiency of our proposed work.
6 citations
TL;DR: A benchmark study of modern distributed databases (DDBs) (e.g., Cassandra, MongoDB, Redis, and MySQL) is an important source of information for selecting the right technology for managing data in edge-cloud deployments as discussed by the authors .
Abstract: A benchmark study of modern distributed databases (DDBs) (e.g., Cassandra, MongoDB, Redis, and MySQL) is an important source of information for selecting the right technology for managing data in edge–cloud deployments. While most of the existing studies have investigated the performance and scalability of DDBs in cloud computing, there is a lack of focus on resource utilization (e.g., energy, bandwidth, and storage consumption) of workload offloading for DDBs deployed in edge–cloud environments. For this purpose, we conducted experiments on various physical and virtualized computing nodes, including variously powered servers, Raspberry Pi, and hybrid cloud (OpenStack and Azure). Our extensive experimental results reveal insights into which database under which offloading scenario is more efficient in terms of energy, bandwidth, and storage consumption.
TL;DR: In this paper , the authors present the key components of SDTs into four layers of technologies: (i) data acquisition, (ii) spatial database management, (iii) GIS middleware software, maps, and (iv) key functional components such as visualizing, querying, mining, simulation and prediction.
Abstract: A Digital Twin (DT) is a virtual replica of a physical object or system, created to monitor, analyze, and optimize its behavior and characteristics. A Spatial Digital Twin (SDT) is a specific type of digital twin that emphasizes the geospatial aspects of the physical entity, incorporating precise location and dimensional attributes for a comprehensive understanding within its spatial environment. The current body of research on SDTs primarily concentrates on analyzing their potential impact and opportunities within various application domains. As building an SDT is a complex process and requires a variety of spatial computing technologies, it is not straightforward for practitioners and researchers of this multi-disciplinary domain to grasp the underlying details of enabling technologies of the SDT. In this paper, we are the first to systematically analyze different spatial technologies relevant to building an SDT in layered approach (starting from data acquisition to visualization). More specifically, we present the key components of SDTs into four layers of technologies: (i) data acquisition; (ii) spatial database management \&big data analytics systems; (iii) GIS middleware software, maps \&APIs; and (iv) key functional components such as visualizing, querying, mining, simulation and prediction. Moreover, we discuss how modern technologies such as AI/ML, blockchains, and cloud computing can be effectively utilized in enabling and enhancing SDTs. Finally, we identify a number of research challenges and opportunities in SDTs. This work serves as an important resource for SDT researchers and practitioners as it explicitly distinguishes SDTs from traditional DTs, identifies unique applications, outlines the essential technological components of SDTs, and presents a vision for their future development along with the challenges that lie ahead.