Effect of redundancy on mean time to failure of wireless sensor networks

Speer, Anh Phan; Chen, I.-R.

doi:10.1109/aina.2006.160

Cited by 9 publications

(5 citation statements)

References 13 publications

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“…P Failure is probability of sensor node failures which is typically 10 -6 in WSNs [41], and P IB_L(i?1) is probability of input queue in receiver node to be filled. After calculation of packet transfer rate by CH node, X T_Li , in Eq.…”

Section: Calculation Of Ch To Sink Delivery Probabilitymentioning

confidence: 99%

An Efficient Data Aggregation Method for Event-Driven WSNs: A Modeling and Evaluation Approach

Kamarei

Hajimohammadi

Patooghy

et al. 2015

Wireless Pers Commun

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This paper proposes and models an efficient data aggregation method for wireless sensor networks (WSNs). In the proposed data aggregation method, every cluster head (CH) node incorporates a local forwarding history to decide whether to forward or to drop a recently received packet. When a new packet arrives at a CH node, a threshold value is calculated based on the information of the forwarding history; then, a random number is generated and compared with the threshold value to determine whether the packet should be dropped or not. In fact, the CH node forwards the new packet with the probability of 1 -p and drops it with probability of p where the parameter p is determined based on the forwarding history. In order The submitted manuscript is an extended version of the work previously proposed in [1]. The extensions of the current version include: (1) a wide range of simulation experiments added to the current version. To do this the proposed data aggregation method is compared with previously proposed methods i.e., RAG, SPEED, DASDR, and RDAG is terms of network performance, data aggregation rate, event miss rate. The previous version of the paper does not offer any comparison experiments, (2) an analytical performance model is proposed and validated in the paper. The proposed model estimates network performance, data aggregation rate, event miss rate of WSN using the proposed data aggregation method with an acceptable prediction error. The model provides a remarkable speed up in the evaluation process of data aggregation methods in the field of wireless sensor networks.to evaluate the proposed data aggregation method, two approaches consisting of simulation and analytical modeling are used. Various scenarios are considered in simulations conducted with NS2 software to compare the proposed data aggregation method with four previously proposed methods. Results reveal that the proposed method (1) aggregates more than 70 % of redundant packets, (2) reduces the network end-to-end delay by at least 22 %, and (3) reduces the missed event rate compared with the other methods. The proposed method is also evaluated by means of an analytical model based on queuing networks. The model accurately estimates the network performance utilizing the proposed data aggregation method. Comparisons of the results obtained by the proposed model and simulations confirm that the proposed model has at most 7 % prediction error. The proposed model allows WSN designers to easily achieve useful information about their networks before the establishment and manufacturing of the networks.

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Section: Calculation Of Ch To Sink Delivery Probabilitymentioning

confidence: 99%

An Efficient Data Aggregation Method for Event-Driven WSNs: A Modeling and Evaluation Approach

Kamarei

Hajimohammadi

Patooghy

et al. 2015

Wireless Pers Commun

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“…Caused by low cost design, inaccurate or failed sensor readings increase the false alarm probability. Appropriate fusion of redundant information positively effects the mean time to failure (Speer & Chen, 2006). Voting algorithms exploit redundant information in sensor networks to compare measurements of several nodes (Krasniewski, Varadharajan, Rabeler, Bagchi, & Hu, 2005;Luo, Dong, & Huang, 2006;Sun, Chen, Han, & Gerla, 2005).…”

Section: Related Workmentioning

confidence: 99%

Self-Adapting Event Configuration in Ubiquitous Wireless Sensor Networks

Maaser

Langendoerfer

Ortmann

2010

International Journal of Adaptive, Resilient and Autonomic Systems

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Wireless Sensor Networks are the key-enabler for low cost ubiquitous applications in the area of homeland security, health-care, and environmental monitoring. A necessary prerequisite is reliable and efficient event detection in spite of sudden failures and environmental changes. Due to the fact that the sensors need to be low cost, they have only scarce resources leading to a certain level of failures of sensor nodes or sensing devices attached to the nodes. Available fault tolerant solutions are mainly customized approaches that revealed several shortcomings, particularly in adaptability and energy efficiency. The authors present a complete event detection concept including all necessary steps from formal event definition to autonomous device configuration. It features an event definition language that allows defining complex events as well as enhance the reliability by tailor-made voting schemes and application constraints. Based on that, this paper introduces a novel approach for self-adapting on-node and in-network processing, called Event Decision Tree (EDT). EDT autonomously adapts to available resources and environmental conditions, even though it requires to (re-)organize collaboration between neighboring nodes for evaluation. The authors’ approach achieves fine-grained event-related fault tolerance with configurable adaptation rate while enhancing maintainability and energy efficiency.

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“…To reduce redundancy, they use the SVD method for subset selection and turn off other nodes. This paper is about reducing redundancy and not about estimating how much data redundancy is there in the network [18] analyzes the effect of redundancy on the mean time to failure of a network of wireless sensors in terms of energy-depletion. The authors show that there is a tradeoff between the degree of redundancy and the mean time to failure (MTTF).…”

Section: Related Workmentioning

confidence: 99%

Probability Model for Data Redundancy Detection in Sensor Networks

Kumar

Park

2009

Mobile Information Systems

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Sensor networks are made of autonomous devices that are able to collect, store, process and share data with other devices. Large sensor networks are often redundant in the sense that the measurements of some nodes can be substituted by other nodes with a certain degree of confidence. This spatial correlation results in wastage of link bandwidth and energy. In this paper, a model for two associated Poisson processes, through which sensors are distributed in a plane, is derived. A probability condition is established for data redundancy among closely located sensor nodes. The model generates a spatial bivariate Poisson process whose parameters depend on the parameters of the two individual Poisson processes and on the distance between the associated points. The proposed model helps in building efficient algorithms for data dissemination in the sensor network. A numerical example is provided investigating the advantage of this model.

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Effect of redundancy on mean time to failure of wireless sensor networks

Cited by 9 publications

References 13 publications

An Efficient Data Aggregation Method for Event-Driven WSNs: A Modeling and Evaluation Approach

An Efficient Data Aggregation Method for Event-Driven WSNs: A Modeling and Evaluation Approach

Self-Adapting Event Configuration in Ubiquitous Wireless Sensor Networks

Probability Model for Data Redundancy Detection in Sensor Networks

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