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.

Ovarian cancer ranks as the fifth leading cause of cancer-related mortality in women. Late-stage diagnosis (stages III and IV) is a major challenge due to the often vague and inconsistent initial symptoms. Current diagnostic methods, such as biomarkers, biopsy, and imaging tests, face limitations, including subjectivity, inter-observer variability, and extended testing times. This study proposes a novel convolutional neural network (CNN) algorithm for predicting and diagnosing ovarian cancer, addressing these limitations. In this paper, CNN was trained on a histopathological image dataset, divided into training and validation subsets and augmented before training. The model achieved a remarkable accuracy of 94%, with 95.12% of cancerous cases correctly identified and 93.02% of healthy cells accurately classified. The significance of this study lies in overcoming the challenges associated with the human expert examination, such as higher misclassification rates, inter-observer variability, and extended analysis times. This study presents a more accurate, efficient, and reliable approach to predicting and diagnosing ovarian cancer. Future research should explore recent advances in this field to enhance the effectiveness of the proposed method further.

The interaction among different Internet of Things (IoT) sensors and devices become massive and insecure over the Internet as we probe to smart cities. These heterogeneous devices produce an enormous amount of data that is vulnerable to various malicious threats. The generated data need to be processed and analyzed in a secure fashion to make smart decisions. The smart urban planning is becoming a reality through the mass information generated by the Internet of Things (IoT). This paper exhibits a novel architecture, SafeCity, that limelight the ecosystem of smart cities consists of cameras, sensors, and other real-world physical devices. SafeCity is a three-layer architecture, i.e., a data security layer, a data computational layer, and a decision-making layer. At the first layer, payload-based symmetric encryption is used to secure the data from intruders by exchanging only the authentic data among the physical devices. The second layer is used for the computation of secured data. Finally, the third layer extracts visions from data. The secured exchange of data is ensured by using Raspberry Pi boards while the computation of data is tested on trustworthy datasets, using the Hadoop platform. The assessments disclose that SafeCity presents precious insights into a secured smart city in the context of sensors based IoT environment.

In recent years, Federated Learning (FL) has gained traction as a privacy-centric approach in medical imaging. This study explores the challenges posed by data heterogeneity on FL algorithms, using the COVIDx CXR-3 dataset as a case study. We contrast the performance of the Federated Averaging (FedAvg) algorithm on non-identically and independently distributed (non-IID) data against identically and independently distributed (IID) data. Our findings reveal a notable performance decline with increased data heterogeneity, emphasizing the need for innovative strategies to enhance FL in diverse environments. This research contributes to the practical implementation of FL, extending beyond theoretical concepts and addressing the nuances in medical imaging applications. This research uncovers the inherent challenges in FL due to data diversity. It sets the stage for future advancements in FL strategies to effectively manage data heterogeneity, especially in sensitive fields like healthcare.

Internet of Things (IoT) has widely been accepted as a promising paradigm for connecting a large number of resource-constrained miniature sensor nodes that have the ability to sense the deployed environment. They have found their applications in various aspect of our daily lives. However, these nodes are mostly restricted to sense only the scalar data. Nowadays, multimedia sensor nodes are gaining significant attention due to their ability to collect scalar as well as multi-dimensional data. These nodes are considered as the foundation of Internet of Multimedia Things (IoMT) and are shaping the perception of IoT. Multimedia data have stringent requirements in terms of reliability, latency, storage, bandwidth, and Quality of Service (QoS). To provide seamless and interoperable communication in IoMT, the underlying protocol stacks need to fulfill these stringent requirements. However, the heterogeneous nature of multimedia sensors makes interoperability a challenging task to fulfill. To understand the challenges faced by seamless and interoperable communication in IoMT, we provide a comprehensive review of the existing protocol stacks of IoMT and analyze their feasibility for multimedia streaming applications. Data storage of multimedia applications is another area that requires immediate attention of the research community. For this purpose, we study cloud as an entity to facilitate multimedia applications of IoMT. The instances of multimedia cloud are analyzed and a number of shortcomings are identified that pave the way for edge computing in IoMT. Finally, we present a case study that shows the significance of our work. The case study portrays an in-home patient monitoring system with an interoperable communication among the connected multimedia streaming devices at home, and healthcare practitioners at hospital. The case study also highlights the importance of uninterrupted data storage and retrieval at the network edge and multimedia sensor nodes.