Out of the many computing and software oriented models that are being adopted by Computer Networking, Fog Computing has captured quite a wide audience in Research and Industry. There is a lot of confusion on its precise definition, position, role and application. The Internet of Things (IOT), todays' digitized intelligent connectivity domain, demands real time response in many applications and services. This renders Fog Computing a suitable platform for achieving goals of autonomy and efficiency. This paper is a justification of the concepts, interest, approaches, and practices of Fog Computing. It describes the need for adopting this new model and investigate its prime features by elucidating the scenarios for implementing it, thereby outlining its significance in the IoT world.

The Internet of Things (IoT) epitomizes the upcoming eminent transition in the world's economy and human lifestyle where people and various objects are correlated within networks. Data Aggregation is a technique which can be used to mitigate Big Data challenges within IoT. This paper provides an overview of various approaches for aggregation of data in IoT infrastructure. A new class of reliable Data Aggregation algorithm is discussed as well. This new class of algorithm uses a consensus based aggregation with fault tolerance methodology in Fog Computing. The new approach allows promoting adaptive behavior and more efficient delivery of the aggregation outcomes to the ascendant node(s). The proposed method is fault tolerant and deals with nodes reliability issues.

Link quality protocols employ link quality estimators to collect statistics on the wireless link either independently or cooperatively among the sensor nodes. Furthermore, link quality routing protocols for wireless sensor networks may modify an estimator to meet their needs. Link quality estimators are vulnerable against malicious attacks that can exploit them. A malicious node may share false information with its neighboring sensor nodes to affect the computations of their estimation. Consequently, malicious node may behave maliciously such that its neighbors gather incorrect statistics about their wireless links. This paper aims to detect malicious nodes that manipulate the link quality estimator of the routing protocol. In order to accomplish this task, MINTROUTE and CTP routing protocols are selected and updated with intrusion detection schemes (IDSs) for further investigations with other factors. It is proved that these two routing protocols under scrutiny possess inherent susceptibilities, that are capable of interrupting the link quality calculations. Malicious nodes that abuse such vulnerabilities can be registered through operational detection mechanisms. The overall performance of the new LQR protocol with IDSs features is experimented, validated and represented via the detection rates and false alarm rates.

The world today, as we know it, is profuse with information about humans and objects. Datasets generated by cyber-physical systems are orders of magnitude larger than their current information processing capabilities. Tapping into these big data flows to uncover much deeper perceptions into the functioning, operational logic and smartness levels attainable has been investigated for quite a while. Knowledge Discovery & Representation capabilities across mutiple modalities holds much scope in this direction, with regards to their information holding potential. This paper investigates the applicability of an arithmetic tool Tensor Decompositions and Factorizations in this scenario. Higher order datasets are decomposed for Anomaly Pattern capture which encases intelligence along multiple modes of data flow. Preliminary investigations based on data derived from Smart Grid Smart City Project are compliant with our hypothesis. The results proved that Abnormal patterns detected in decomposed Tensor factors encompass deep information energy content from Big Data as efficiently as other Pattern Extraction and Knowledge Discovery frameworks, while salvaging time and resources.

Supply chain cybersecurity is a growing concern for businesses as they utilize increasingly interconnected digital systems. This systematic literature review examines how machine learning (ML) and business intelligence (BI) may be used in conjunctions to improve supply chain cyber security risk management. This review followed PRISMA guidelines. A quality evaluation was performed based on CASP to evaluate 35 peer-reviewed articles published in 2016–2025. The review analysis indicates that although ML has been extensively utilized for threat detection, BI utilization is fragmented. Additionally, there is a lack of integrated ML-BI frameworks, specifically for small–medium enterprises (SMEs) and developing economies. As such, this literature review provides a conceptual four-layer framework of predictive and analytical capabilities for threat detection, risk assessment, and decision-making. It also identifies a structured research agenda with which to advance the field of research.

This study presents a comprehensive examination of the most recent developments and challenges related to implementing artificial intelligence models for sentiment analysis using cross-lingual transfer learning techniques. This paper aims to provide an overview of the historical context and theoretical foundations of sentiment analysis, cross-lingual transfer learning, and the significance of Artificial Intelligence (AI) models. It will also discuss the latest advancements and cutting-edge methodologies in these domains. In addition, we engage in examine research methods, establishing assessment criteria, identifying developing trends, and the identification of persistent issues. Furthermore, we explore the implications of cross-lingual sentiment analysis across many fields.