Abstract:In different protocols, wireless sensor networks (WSNs) form different topologies. The reliability evaluation of a WSN whose topology is a chain is studied in this paper. A bidirectional data transmission tree (BDTT) model is established to describe the process of data fusion and transmission on a WSN with a chain structure. From the characteristics of the measurement errors, the number of fusions, and measurement credibility, 3 definitions for the reliability of a BDTT are presented. Based on the universal ge… Show more
“…Studies, evaluations, and analyses of WSNs' reliability have been previously carried out in several studies [26,27]. Evaluation of the reliability of a WSN can be conducted utilizing different techniques, of which the most well-known are a universal generating function [28], a Markov model [29], a fault tree [30], and a Monte Carlo simulation [31], where the authors utilized an ordered binary decision diagram (OBDD) beside a Monte Carlo simulation, to evaluate the WSN's reliability. The authors of [32] proposed a Monte Carlo Markov chain simulation method to evaluate the reliability, regarding data capacity and coverage area, of mobile WSNs with multi-state nodes, but the power consumption was not taken into account.…”
There are many different fields in which wireless sensor networks (WSNs) can be used such as environmental monitoring, healthcare, military, and security. Due to the vulnerability of WSNs, reliability is a critical concern. Evaluation of a WSN’s reliability is essential during the design process and when evaluating WSNs’ performance. Current research uses the reliability block diagram (RBD) technique, based on component functioning or failure state, to evaluate reliability. In this study, a new methodology-based RBD, to calculate the energy reliability of various proposed chain models in WSNs, is presented. A new method called D-Chain is proposed, to form the chain starting from the nearest node to the base station (BS) and to choose the chain head based on the minimum distance D, and Q-Chain is proposed, to form the chain starting from the farthest node from the BS and select the head based on the maximum weight, Q. Each chain has three different arrangements: single chain/single-hop, multi-chain/single-hop, and multi-chain/multi-hop. Moreover, we applied dynamic leader nodes to all of the models mentioned. The simulation results indicate that the multi Q-Chain/single-hop has the best performance, while the single D-Chain has the least reliability in all situations. In the grid scenario, multi Q-Chain/single-hop achieved better average reliability, 11.12 times greater than multi D-Chain/single-hop. On the other hand, multi Q-Chain/single-hop achieved 6.38 times better average reliability than multi D-Chain/single-hop, in a random scenario.
“…Studies, evaluations, and analyses of WSNs' reliability have been previously carried out in several studies [26,27]. Evaluation of the reliability of a WSN can be conducted utilizing different techniques, of which the most well-known are a universal generating function [28], a Markov model [29], a fault tree [30], and a Monte Carlo simulation [31], where the authors utilized an ordered binary decision diagram (OBDD) beside a Monte Carlo simulation, to evaluate the WSN's reliability. The authors of [32] proposed a Monte Carlo Markov chain simulation method to evaluate the reliability, regarding data capacity and coverage area, of mobile WSNs with multi-state nodes, but the power consumption was not taken into account.…”
There are many different fields in which wireless sensor networks (WSNs) can be used such as environmental monitoring, healthcare, military, and security. Due to the vulnerability of WSNs, reliability is a critical concern. Evaluation of a WSN’s reliability is essential during the design process and when evaluating WSNs’ performance. Current research uses the reliability block diagram (RBD) technique, based on component functioning or failure state, to evaluate reliability. In this study, a new methodology-based RBD, to calculate the energy reliability of various proposed chain models in WSNs, is presented. A new method called D-Chain is proposed, to form the chain starting from the nearest node to the base station (BS) and to choose the chain head based on the minimum distance D, and Q-Chain is proposed, to form the chain starting from the farthest node from the BS and select the head based on the maximum weight, Q. Each chain has three different arrangements: single chain/single-hop, multi-chain/single-hop, and multi-chain/multi-hop. Moreover, we applied dynamic leader nodes to all of the models mentioned. The simulation results indicate that the multi Q-Chain/single-hop has the best performance, while the single D-Chain has the least reliability in all situations. In the grid scenario, multi Q-Chain/single-hop achieved better average reliability, 11.12 times greater than multi D-Chain/single-hop. On the other hand, multi Q-Chain/single-hop achieved 6.38 times better average reliability than multi D-Chain/single-hop, in a random scenario.
“…In [33] the reliability and availability of several linear sensor network architectures are compared. In [34] a bidirectional data transmission tree model is used to evaluate the reliability of a WSN with a chain structure. The end-to-end reliability of IoT is also investigated in [35] using a reliability block diagram method.…”
Section: Classification Of Previous Workmentioning
Wireless Sensor Networks are subjected to some design constraints (e.g., processing capability, storage memory, energy consumption, fixed deployment, etc.) and to outdoor harsh conditions that deeply affect the network reliability. The aim of this work is to provide a deeper understanding about the way redundancy and node deployment affect the network reliability. In more detail, the paper analyzes the design and implementation of a wireless sensor network for low-power and low-cost applications and calculates its reliability considering the real environmental conditions and the real arrangement of the nodes deployed in the field. The reliability of the system has been evaluated by looking for both hardware failures and communication errors. A reliability prediction based on different handbooks has been carried out to estimate the failure rate of the nodes self-designed and self-developed to be used under harsh environments. Then, using the Fault Tree Analysis the real deployment of the nodes is taken into account considering the Wi-Fi coverage area and the possible communication link between nearby nodes. The findings show how different node arrangements provide significantly different reliability. The positioning is therefore essential in order to obtain maximum performance from a Wireless sensor network.
“…Based on the system description in Section 2, this paper constructs a generator capacity distribution function and a node demand distribution function using UGF (Lisnianski, 2007;Lisnianski & Ding, 2009;Meena & Vasanthi, 2016;Khorshidi et al, 2016;Liu et al, 2017).…”
Section: Generator Capacity Distribution and Node Demand Distributionmentioning
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