Enhancing Fault Detection in Wireless Sensor Networks using Digital Twin and Predictive Maintenance

A new hierarchical belief-rule-based method for reliability evaluation of wireless sensor network

A wireless sensor network (WSN) (singular and plural of acronyms are spelled the same) is a distributed system composed of autonomous sensor nodes wireless connected and randomly scattered into a geographical area to cooperatively monitor physical or environmental conditions. Adequate techniques and strategies are required to manage a WSN so that it works properly, observing specific quantities and metrics to evaluate the WSN operational conditions. Among them, one of the most important is the reliability. Considering a WSN as a system composed of sensor nodes the system reliability approach can be applied, thus expressing the WSN reliability in terms of its nodes' reliability. More specifically, since often standby power management policies are applied at node level and interferences among nodes may arise, a WSN can be considered as a dynamic system. In this article we therefore consider the WSN reliability evaluation problem from the dynamic system reliability perspective. Static–structural interactions are specified by the WSN topology. Sleep/wake-up standby policies and interferences due to wireless communications can be instead considered as dynamic aspects. Thus, in order to represent and to evaluate the WSN reliability, we use dynamic reliability block diagrams and Petri nets. The proposed technique allows to overcome the limits of Markov models when considering non-linear discharge processes, since they cannot adequately represent the aging processes. In order to demonstrate the effectiveness of the technique, we investigate some specific WSN network topologies, providing guidelines for their representation and evaluation.

In this paper we consider the problem of modeling and evaluating the infrastructure communication reliability (ICR) of wireless sensor networks (WSN). Our approach is progressive and based on reduced ordered binary decision diagrams (ROBDD). Due to the nature of the ROBDD and the reduction scheme used in the progressive process, the approach is computationally efficient and is easy to implement. We also incorporate the consideration of common cause failures (CCF) into the reliability analysis of WSN. We illustrate the application and advantages of our approach by working through the analysis of an example WSN

One of the remarkable challenges in the area of Wireless Sensor Networks (WSNs) is how to route the collected data and transfer them in the nodes. Due to the high rate of packet loss, guaranteeing reliable and efficient packet transfer and delivery is considered to be one of the hot issues in the area of WSNs. Indeed, due to energy limitation of nodes in WSNs, reliability and energy are regarded as critical parameters in these networks. In this paper, a method has been proposed for optimizing the parameters of reliability and energy consumption in multimedia WSNs. In the proposed protocol, Increasing the Delivery Ratio with Network Coding (IDRNC) a network coding mechanism has been used for transmitting data which enhances the reliability of the network. Furthermore, reducing the number of transmissions and receptions in network nodes results in the reduction of energy consumption. In the proposed protocol, the transmitted packets are first coded and transmitted to the sink through the shortest route. In the middle of the route, in case packets are lost, median nodes can detect and recover the lost packet through the coded packet. The simulation results indicated that the method proposed in this study operates better than the LIEMRO method with respect to the following features: system efficiency, average remaining energy of the network, the number of lost packets and reliability. Keywords: Multipath Routing, Network Coding, Reliability, Wireless Multimedia Sensor Networks (WMSNs)

Wireless Sensor Network (WSN) consists numerous sensor hubs which containing a preparing unit, at least one sensor, a radio for information correspondence and power unit generally outfitted with a low limit energy distributed over a geographic area for monitoring our environment and physical conditions. It has been established that vitality is the most obliging element on the functionality of such systems as they are controlled with constrained vitality and replacement of vitality resources might be difficult. While sending the data in sensor network, there might be loss of information or miscalculation could occur in receiving data during transferring. The correctness of information has incredible impact on the performance of the network. To enhance the exactness of sensor information, minimizing vitality utilization and adaptation to internal failure is vital for some WSN’s applications as they operate in unpredictable conditions and ought to stay operational regardless of whether a network failure happen. This paper surveys the available energy efficient, reliability and fault tolerant in WSNs. It focuses on Residue Number System (RNS) and Agent technologies for energy-efficient and fault tolerant in WSNs respectively. However, performance evaluation was also conducted based on the energy consumption, reliability, delay in receiving the sent data and efficiency.

Wireless sensor networks are used for sensing data if wired communication is not suitable. Connection losses and depletion of nodes, however, result in reduced data availability of such networks. This is problematic in upcoming scenarios like health care or home automation where data availability is highly important. In this paper, we present an approach to provide robust data storage for wireless sensor networks. We achieve this goal by providing FAME-DBMS, a customizable database management system which can be tailored according to the varying requirements of a sensor network. FAME-DBMS provides reliable data storage using security and integrity features, transaction management and recovery and a customizable query engine. Since data reliability in wireless sensor networks also suffers from node failures, we propose a new S - RAID storage layer inspired from the RAID approach of server systems. This S-RAID is integrated in FAME-DBMS to store data redundantly and distributed in a wireless sensor network and thus provides access to data even when nodes fail.

Design of reliable wireless sensor network (WSN) needs to address the failure of single or multiple network components and implementation of the techniques to tolerate the faults occurred at various levels. The issues and requirements of reliability improvement mechanism depend on the available resources and application for which the WSN is deployed. This paper discusses the different modeling approaches to evaluate the reliability and classification of the approaches to improve it. Also the paper analyzes reliability enhancement by existing fault tolerant methods in WSN and compares the performance of these techniques with the technique we developed. From the results of the analysis we highlight the challenges and the characteristics of the sensor network affects the reliability and give some scope of future research directions in order to enhance reliability.

WITHDRAWN: Developing a software reliability evaluation model for wireless sensor networks based on analysis of software fault growth

In this study a model for data reliability in wireless sensor networks is proposed, in which machine learning methods are used. Proposed framework includes data modelling, missing data prediction, anomaly detection, data fusion and trust mechanism phases. Thus, temporal analysis is performed on the preprocessed sensor data and missing data are predicated. Then outliers on collected data are detected on the cluster head nodes by using Eta one-class Support Vector Machines. If an event is detected data are fused and then send to sink. If an anomaly is detected for a node's data, the trust weight of the node is decreased.

Wireless Sensor Networks (WSNs) have permeated various domains, such as industrial automation, healthcare, and environmental monitoring, due to their ability to effectively collect and communicate data in diverse environments. The reliability and durability of WSNs have become paramount, particularly in critical applications. Predictive maintenance, which anticipates faults and mitigates them preemptively, emerges as a pivotal strategy to enhance network longevity and reliability. This paper explores using machine learning (ML) approaches for predictive maintenance in WSNs. Various ML algorithms, including regression, classification, and clustering, are investigated in terms of their efficacy in predicting and diagnosing issues within the network, thereby facilitating timely interventions. The paper contributes a novel architecture integrating ML models into WSNs to monitor, analyze, and predict potential failures, ensuring optimal network functionality.

Digital twin applications in radiology and radiotherapy: Applications, challenges, and future perspectives.

Wireless Sensor Networks (WSN) are networks composed of spatially distributed, autonomous devices called sensor nodes that monitor and collect data about environmental conditions. These sensor nodes communicate wirelessly to transmit data to a central system for analysis. WSNs use these numerous nodes distributed in a network to perform various tasks such as data collection, processing, organization, communication, management, etc., and energy is the most crucial resource for properly functioning these nodes over the network. Hence, its primary concern is optimizing energy consumption and extending the network lifespan. The sensor nodes can be clustered to increase their lifespan further. A secondary concern is a random distribution of sensor nodes within a predefined network area which presents challenges such as incomplete coverage, connectivity issues, and inefficient energy consumption. The proposed system detects coverage holes and improves node participation by placing monitor nodes strategically within the network to ensure complete coverage and combines it with an energy-efficient routing approach and optimized Shortest Path Routing through minimum spanning trees. This combination minimizes distances between nodes, reducing energy consumption and enhancing network efficiency.

This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.

Data reliability in Wireless Sensor Networks (WSNs) has a substantial influence on their smooth functioning and resource limitations. In a WSN, the data aggregated from clustered sensor nodes are forwarded to the base station for analysis. Anomaly Detection (AD) focuses on detecting outlier data to ensure consistency during data aggregation. As WSNs have critical resource limitations concerning energy consumption and sensor node lifetime, AD is supposed to provide data integrity with minimum energy consumption, which has been an active research problem. Hence, researchers are striving for methods to improve the accuracy of data handled with a concern on the constraints of WSNs. This paper introduces a Feed-forward Autoencoder Neural Network (FANN) model to detect abnormal instances with improved accuracy and reduced energy consumption. The proposed model also acts as a False Positive Reducer intending to reduce false alarms. It has been compared with the other dominant unsupervised algorithms over robustness and other significant metrics with real-time datasets. Relatively, our proposed model yields an improved accuracy with fewer false alarms thereby supporting a sustainable WSN.

Emerging Industry 4.0 applications require ever-increasing amounts of data and new sources of information to more accurately characterize the different processes of a production line. Industrial Internet of Things (IIoT) technologies, and in particular Wireless Sensor Networks (WSNs), allow a large amount of data to be digitized at a low energy cost, thanks to their easy scalability and the creation of meshed networks to cover larger areas. In industry, data acquisition systems must meet certain reliability and robustness requirements, since other systems such as predictive maintenance or the digital twin, which represents a virtual mapping of the system with which to interact without the need to alter the actual installation, may depend on it. Thanks to the IEEE 802.15.4e standard and the use of Time-Slotted Channel Hopping (TSCH) as the medium access mechanism and IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) as the routing protocol, it is possible to deploy WSNs with high reliability, autonomy, and minimal need for re-configuration. One of the drawbacks of this communication architecture is the low efficiency of its deployment process, during which it may take a long time to synchronize and connect all the devices in a network. This paper proposes an analytical model to characterize the process for the creation of downstream routes in RPL, whose transmission of multi-hop messages can present complications in scenarios with a multitude of interfering nodes and resource allocation based on minimal IPv6 over the TSCH mode of IEEE 802.15.4e (6TiSCH). This type of multi-hop message exchange has a different behaviour than the multicast control messages exchanged during the synchronization phase and the formation of upstream routes, since the number of interfering nodes changes in each retransmission.