Border Effect Analysis for Reliability Assurance and Continuous Connectivity of Wireless Sensor Networks in the Presence of Sensor Failures

Laranjeira, Luiz A.; Rodrigues, Genaína Nunes

doi:10.1109/twc.2014.2314102

Cited by 36 publications

(31 citation statements)

References 28 publications

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“…Connectivity reliability [1][2][3][4][5][6][7] investigates the probability that the network is still connected for a given period of time under the case of some nodes or links failure. Performance reliability [8,9] analyzes the end-to-end delay, packet delivery rate, and other network parameters.…”

Section: Related Workmentioning

confidence: 99%

Transmission Reliability Evaluation for Wireless Sensor Networks

Zhu

Lü

Han

et al. 2016

International Journal of Distributed Sensor Networks

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Network reliability of Wireless Sensor Networks (WSNs) is difficult to be evaluated because of its complexity, multistates, and dynamic characteristics. To satisfy the user's need of reliability evaluation for network transmission we propose some evaluation models and a dynamical evaluation framework. The evaluation models are mission-oriented and based on transmission paths (uplink and downlink). The dynamic evaluation framework can be on-demand customized and it will be autoupdated once the communication environment changes or the nodes fail. Finally, we simulated clustered and mesh WSNs with NS-2. Simulation result shows that the proposed evaluation framework was effective and accurate.

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Section: Related Workmentioning

confidence: 99%

Transmission Reliability Evaluation for Wireless Sensor Networks

Zhu

Lü

Han

et al. 2016

International Journal of Distributed Sensor Networks

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“…In the similar way, the average effective coverage area φ cor for nodes in the corner border C and the expected effective node degree E cor for nodes in the lateral corner region C are obtained as [9]:…”

Section: A Square Border Effectmentioning

confidence: 99%

“…Since it is known in [9], [10] that the number of neighbors of a node located in border areas is smaller than that located in the inner area, i.e., the border effect is effective, we compare the expected number of neighbors in both types of network area in Fig. 7.…”

Section: A Comparison Of Network Survivabilitymentioning

confidence: 99%

“…In other words, the border effects in communication network area tend to decrease both the communication coverage and the node degree, which reflect the whole network availability. Recently, Laranjeira and Rodrigues [9] show that the relative average node degree for nodes in borders is independent of the node transmission range and of the overall network node density in a square communication network area. Bettsetetter [10] also gives a formula to calculate the average node degree for nodes in borders for a circular communication network area.…”

Section: Introductionmentioning

confidence: 99%

“…More specifically, we represent an approximate form of network survivability with the expected number of active nodes, instead of its upper and lower bounds, and consider border effects in both two types of communication network areas; square area [9] and circular area [10]. The remaining part of this paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

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Survivability Quantification of Wireless Ad Hoc Network Taking Account of Border Effects

Zhang

2015

2015 IEEE 21st Pacific Rim International Symposium on Dependable Computing (PRDC)

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Border effect in communication network area is one of the most important problems to quantify accurately the performance/dependability of wireless ad hoc networks (WAHNs), because the assumption on uniformity of network node density is often unrealistic to describe the actual communication area. This problem appears in modeling the node behavior of WAHNs and in quantification of their network survivability. In this paper we focus on the border effects, and reformulate the network survivability models based on a semi-Markov process, where two kinds of communication network areas are considered; square area and circular area. Based on some geometric ideas, we improve the quantitative network survivability measures for three stochastic models taking account of the border effects.In numerical examples, we compare our new models with the existing ones without border effects in both steady-state and transient network survivability analyses. We show through a simulation study that the border effects are significant factors in quantifying network survivability.

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Computational Intelligence for Simulating a LiDAR Sensor

Castaño

Beruvides

Villalonga

et al. 2019

Sensor Systems Simulations

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In this chapter, an overview of some of the most commonly Computational Intelligence techniques used to provide new capabilities to sensor networks in Cyber-Physical and Internet-of-Things environments, and for verifying and evaluating the reliability issues of sensor networks is presented. Nowadays, on-chip Light Detection and Ranging (LiDAR) concept has driven a great technological challenge into sensor networks application for Cyber-Physical and Internet-of-Things systems. Therefore, the modelling and simulation of a LiDAR sensor networks is also included in this chapter that is structured as follows. First, a brief description of the theoretical modelling of the mathematical principle of operation is outlined. Subsequently, a review of the state-of-art of Computational Intelligence techniques in sensor system simulations is explained. Likewise, a use case of applying computational intelligence techniques to LiDAR sensor networks in a Cyber-Physical System environment is presented. In this use case, a model library with four specific Artificial Intelligence-based methods is also designed based on sensory information database provided by the LiDAR simulation. Some of them are multi-layer perceptron neural network, a self-organization map, a support vector machine and a k-nearest neighbours. The results demonstrate the suitability of using Computational Intelligence methods to increase the reliability of sensor networks when addressing the key challenges of safety and security in automotive applications.

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Border Effect Analysis for Reliability Assurance and Continuous Connectivity of Wireless Sensor Networks in the Presence of Sensor Failures

Cited by 36 publications

References 28 publications

Transmission Reliability Evaluation for Wireless Sensor Networks

Transmission Reliability Evaluation for Wireless Sensor Networks

Survivability Quantification of Wireless Ad Hoc Network Taking Account of Border Effects

Computational Intelligence for Simulating a LiDAR Sensor

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