Cyber-Enabled Product Lifecycle Management: A Multi-Agent Framework

Trouble free use of a product and its associated services for a specified minimum period of time is a major factor to win the customer\u27s trust in the product. Rapid and easy serviceability to maintain its functionalities plays a key role in achieving this goal. However, the sustainability of such a model cannot be promised unless the current health status of the product is monitored and condition-based maintenance is exercised. Internet of Things (IoT), an important connectivity paradigm of recent times, which connects physical objects to the internet for real-time information exchange and execution of physical actions via wired/wireless protocols. While the literature is full of various feasibility and viability studies focusing on architecture, design, and model development aspects, there is limited work addressing an IoT-based health monitoring of systems having high collateral damage. This motivated the research to develop a multi-agent framework for monitoring the performance and predicting impending failure to prevent unscheduled maintenance and downtime over internet, referred to as for cyber-enabled product lifecycle management (C-PLM). The framework incorporates a number of autonomous agents, such as hard agent, soft agent, and wave agent, to establish network connectivity to collect and exchange real-time health information for prognostics and health management (PHM). The proposed framework will help manufacturers not only to resolve the warranty failure issues more efficiently and economically but also improve their corporate image. The framework further leads to efficient handling of warranty failure issues and reduces the chances of future failure, i.e., offering durable products. From the sustainability point of view, this framework also addresses the reusability of the parts that still have a significant value using the prognostics and health data. Finally, multi-agent implementation of the proposed approach using a power substations for IoT-based C-PLM is included to show is efficacy

Machine Learning-assisted Bayesian Inference for Jamming Detection in 5G NR

The increased flexibility and density of spectrum access in 5G NR have made jamming detection a critical research area. To detect coexisting jamming and subtle interference that can affect legitimate communications performance, we introduce machine learning (ML)-assisted Bayesian Inference for jamming detection methodologies. Our methodology leverages cross-layer critical signaling data collected on a 5G NR Non-Standalone (NSA) testbed via supervised learning models, and are further assessed, calibrated, and revealed using Bayesian Network Model (BNM)-based inference. The models can operate on both instantaneous and sequential time-series data samples, achieving an Area under Curve (AUC) in the range of 0.947 to 1 for instantaneous models and between 0.933 to 1 for sequential models including the echo state network (ESN) from the reservoir computing (RC) family, for jamming scenarios spanning multiple frequency bands and power levels. Our approach not only serves as a validation method and a resilience enhancement tool for ML-based jamming detection, but also enables root cause identification for any observed performance degradation. Our proof-of-concept is successful in addressing 72.2\% of the erroneous predictions in sequential models caused by insufficient data samples collected in the observation period, demonstrating its applicability in 5G NR and Beyond-5G (B5G) network infrastructure and user devices

Prediction of Faults in Cellular Networks Using Bayesian Network Model

Cellular network service providers compete with each other for the vast and dynamic market that is characterized by the ever-changing services on offer and technology. These services require very reliable net-works that can meet the customer service level of agreement (SLA). We are motivated by this to model the cellular network service faults and this paper reports on results of faults prediction modelling. Cellular networks are uncertain in their behaviours and therefore we use a Bayesian network to model them. We derive probabilistic models of the cellular network system in which the independence of relations between the variables of inter-est are represented explicitly. We use a directed graph in which two nodes are connected by an edge if one is a direct cause of the other. We present the simulation results of the study

Machine Learning in Wireless Sensor Networks for Smart Cities:A Survey

Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart buildings and patient healthcare monitoring. The small size IoT nodes based on low power Bluetooth (IEEE 802.15.1) standard and wireless sensor networks (WSN) (IEEE 802.15.4) standard are generally used for transmission of data to a remote location using gateways. The WSN based IoT (WSN-IoT) design problems include network coverage and connectivity issues, energy consumption, bandwidth requirement, network lifetime maximization, communication protocols and state of the art infrastructure. In this paper, the authors propose machine learning methods as an optimization tool for regular WSN-IoT nodes deployed in smart city applications. As per the author’s knowledge, this is the first in-depth literature survey of all ML techniques in the field of low power consumption WSN-IoT for smart cities. The results of this unique survey article show that the supervised learning algorithms have been most widely used (61%) as compared to reinforcement learning (27%) and unsupervised learning (12%) for smart city applications