Towards Robust Data Storage in Wireless Sensor Networks

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

The penetration of Wireless Sensor Networks (WSNs) in various applications poses a high demand on reliable data storage, especially considering sensor networks are usually deployed in harsh environment. In this paper, we introduce Heterogeneous Wireless Sensor Networks where robust storage nodes are deployed in sensor networks and data redundancy is utilized through coding techniques, in order to improve the reliability of data storage. Taking into account the cost of both data delivery and storage, we propose an algorithm to jointly optimize data routing and storage node deployment. This problem is a binary non-linear combinatorial optimization, and it is highly non-trivial to design efficient algorithms due to its NP-hardness. By levering the Markov approximation framework, we elaborately deign a Continuous Time Markov Chain (CTMC) based scheduling algorithm to drive the storage node deployment and the corresponding routing strategy. Extensive simulations are performed to verify the efficacy of our algorithm.

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

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.

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.

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)

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 emerged as crucial technologies for various applications, including environmental monitoring, healthcare, and smart cities. However, maintaining the reliability and performance of WSNs is challenging due to faults such as node failures, communication issues, and environmental factors. Machine learning (ML) techniques offer promising solutions for diagnosing and predicting faults in WSNs. This article explores various ML-based fault diagnosis methods, highlighting supervised, unsupervised, and hybrid approaches, and their applications in WSNs. The paper also discusses the advantages, limitations, and future directions for ML in fault diagnosis for WSNs.

The emergence of Wireless Sensor Networks has enabled the detection of potentially dangerous or catastrophic events prior to their actual occurrence. Wireless Sensor Networks rely on sensors that collect data on environmental conditions, allowing an early warning system to be implemented for potentially dangerous or catastrophic events, enabling an organization to take actions to prevent or mitigate these types of events. The technology digital twin has potential to improve ability of a wireless sensor network to detect faults and recover from them before the fault occurs. Digital Twins are real-time representations of physical objects or systems that track activity and behaviour of that physical object or system. By combining the Digital Twin with a Wireless Sensor Network, it is possible to create a model of predictive maintenance that can detect potential fault conditions before they occur by utilizing fault-analysis techniques. By utilizing this model to improve the Wireless Sensor Network, it is possible to improve the reliability and reduce the incidence of natural disasters. This report describes how Digital Twins can be created and how they can be used in Predictive Maintenance to increase the reliability of wireless sensor networks. These findings illustrate the power of such an AI-based approach toward completely transforming fault management within WSNs, toward being more robust, efficient, and reliable sensor networks in today's world.

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

Wireless sensor networks (WSNs) are becoming more prevalent in a wide range of uses, including tracking the environment, medical care, and military monitoring. Yet, security, energy efficiency, and overall efficiency are important concerns for these networks. Ensuring the security of data transmission while maintaining energy efficiency is critical for the longevity and reliability of WSNs. This paper proposes a novel method to enhance both security and efficiency in WSNs done the integration of metaheuristic optimization algorithms. The proposed method leverages advanced optimization techniques such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) to dynamically optimize key parameters, including node deployment, routing protocols, and encryption schemes. By optimizing the network topology, routing paths, and energy consumption strategies, the algorithm significantly improves data security, minimizes energy usage, and enhances network lifespan. The simulation results show that the suggested methodology beats standard methods in terms of throughput, security resilience and energy efficiency. The findings highlight the potential of metaheuristic optimization algorithms as a robust tool for improving WSN security and operational efficiency in real-world applications

Wireless sensor networks (WSN) include numerous sensor nodes deployed to collect data efficiently. Energy constraints and unreliable communication remain critical challenges in WSNs. This research proposes a Fuzzy Trust‐based Hybrid Levy Snake Optimization model to improve energy efficiency, scalability, and reliability in WSNs. The Fuzzy Trust‐based Hybrid Levy Snake Optimization model includes cluster formation, optimal cluster head selection using combined levy flight and snake optimization, and a fuzzy trust mechanism to enhance data security and minimize energy consumption. The algorithm identifies secure and energy‐efficient routing paths by evaluating trust factors and optimizing cluster head selection based on parameters like energy and delay. Extensive experiments validate the Fuzzy Trust‐based Hybrid Levy Snake Optimization model, demonstrating superior performance compared to traditional approaches. The Fuzzy Trust‐based Hybrid Levy Snake Optimization model achieves a packet delivery ratio of 98.5%, throughput of 98.7%, and network lifetime of 9500 ms while reducing energy consumption to 83 J and end‐to‐end delay to 0.01 ms. These findings highlight the Fuzzy Trust‐based Hybrid Levy Snake Optimization model's effectiveness in addressing energy and trust challenges in WSNs, offering a robust solution for secure and efficient data transmission.

Wireless sensor networks (WSNs) have broad application prospects due to having the characteristics of low power, low cost, wide distribution and self-organization. At present, most the WSNs are battery powered, but batteries must be changed frequently in this method. If the changes are not on time, the energy of sensors will be insufficient, leading to node faults or even networks interruptions. In order to solve the problem of poor power supply reliability in WSNs, a novel power supply method, the single-wire power transfer method, is utilized in this paper. This method uses only one wire to connect source and load. According to the characteristics of WSNs, a single-wire power transfer system for WSNs was designed. The characteristics of directivity and multi-loads were analyzed by simulations and experiments to verify the feasibility of this method. The results show that the total efficiency of the multi-load system can reach more than 70% and there is no directivity. Additionally, the efficiencies are higher than wireless power transfer (WPT) systems under the same conductions. The single-wire power transfer method could satisfy the characteristics of WSNs and effectively solve the problem of poor power supply reliability.

In order to make the routing of mobile agents no longer depend on the network structure too much, a routing algorithm, called Stateless Weight Routing for Mobile Agent (SWR-MA), for mobile Agents in Wireless Sensor Networks (WSN) was presented. In SWR-MA, a weight metric was introduced which was related to the position of the nodes. By comparing the weights values, mobile Agents can determine their own routes automatically. SWR-MA can be applied when topological information of WSN changes such as in a network with mobile Sinks. The energy consumptions and the delays of the network can be reduced and the reliability of WSN can be improved by this protocol. Finally, a simulation evaluation was made to validate the performance of SWR-MA.