Modeling Reliability in Wireless Sensor Networks

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

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

As Wireless Sensor Network (WSN) is spread across wide geographically area, global end-to-end estimates of control parameters can't predict timely reporting of data. However, as packet progresses over multihop WSN, routing decisions may have to be adapted according to changed environment parameters/conditions. Many algorithms lack dynamic adaptability to changed environment conditions. Real Time WSN need to trade off latency, path traversed in number of hops and energy cost as per dynamically changing conditions. Earned Value Management (EVM) is project management technique to trade-off schedule, cost and quality constraint. As part of this work, it is hereby suggested to adapt routing decision at each hop based on Earned Value Management of latency, energy cost and path traversed in number of hops at that time. At initial step, End-to-end estimated values for latency, energy cost and path traversed in number of hops are allocated for each flow. Estimate to complete (ETC) values for all parameters are calculated at each hop and used to decide how much is estimated remaining value of all parameters to reach at the destination. Based on ETC values, routing decisions can be adapted. At the end of delivery, variance with planned value is calculated. This value can be used to refine end-to-end estimates. The whole process can be divided into following steps: Estimate End-to-end values for latency, energy cost, number of hops, Estimate to complete (ETC) - Calculate Estimated Values for latency, energy cost, number of hops to reach at destination., Adapt or refine the control parameters such as speed or transmission power as per ETC values for latency, energy cost, number of hops. If destination can't be reached by adapting control parameters, packets are dropped., Calculate Variance with respect to planned values and refine end-to-end estimates Using Earned Value technique helps in tracking the actual progress made till current time. It helps in taking corrective action early; hence it is more cost effective. It also helps in effective usage of limited resources of WSN by dropping packets which can't reach destination within desired latency.

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 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.

WITHDRAWN: Developing a software reliability evaluation model for wireless sensor networks based on analysis of software fault growth

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.

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)

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.

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 (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.

Wireless sensor networks (WSNs) are gaining popularity in a variety of applications, including smart homes, industrial facilities, and environmental monitoring systems. However, using WSNs exposes them to a variety of attacks that might have major effects, such as data loss, privacy breaches, and safety issues. As a result, understanding the sorts of threats and detection/prevention strategies in WSNs is critical to ensuring their security, especially from an educational perspective. We present an overview of the sorts of attacks that can occur in WSNs, including denial of service attacks, spoofing attacks, jamming attacks, physical assaults, and node compromise attacks. We also examine encryption, authentication and authorization procedures, intrusion detection systems, and physical security measures as methods for detecting and blocking attacks in WSNs. We examine the benefits and drawbacks of each strategy and give case studies to show their efficacy. Finally, we propose future research directions, such as the development of more effective intrusion detection systems and the enhancement of physical security measures. We can assure the security and reliability of WSNs in diverse applications by knowing the types of attacks and detection/prevention mechanisms.

For the past few years, centralized decision-making is being used for malicious node identification in wireless sensor networks (WSNs). Generally, WSN is the primary technology used to support operations, and security issues are becoming progressively worse. In order to detect malicious nodes in WSN, a blockchain-routing- and trust-model-based jellyfish search optimizer (BCR-TM-JSO) is created. Additionally, it provides the complete trust-model architecture before creating the blockchain data structure that is used to identify malicious nodes. For further analysis, sensor nodes in a WSN collect environmental data and communicate them to the cluster heads (CHs). JSO is created to address this issue by replacing CHs with regular nodes based on the maximum remaining energy, degree, and closeness to base station. Moreover, the Rivest–Shamir–Adleman (RSA) mechanism provides an asymmetric key, which is exploited for securing data transmission. The simulation outcomes show that the proposed BCR-TM-JSO model is capable of identifying malicious nodes in WSNs. Furthermore, the proposed BCR-TM-JSO method outperformed the conventional blockchain-based secure routing and trust management (BSRTM) and distance degree residual-energy-based low-energy adaptive clustering hierarchy (DDR-LEACH), in terms of throughput (5.89 Mbps), residual energy (0.079 J), and packet-delivery ratio (89.29%).

The demand for wireless sensor networks (WSNs) applied to industrial process monitoring and control should increase as the forth industrial revolution gathers pace. Flexibility and low cost make them the perfect choice for the modern 21st century manufacturing plants. In this paper we describe the tests done with a WSN in an industrial environment and analyze the results to ascertain how much interference the processes and the environment (topology, distances, barriers, machines and people) caused in the radio signal. We also discuss the reliability and applicability of WSNs in process monitoring and control. The results obtained with the measurements indicated a relationship between the Received Signal Strength Indicator (RSSI), the Packet Error Rate (PER), the environment and the processes generating subsidies for the network management system. This article ends with a strategy proposal for the management of WSNs in small and medium-sized industrial environments.