Dynamic timeout for data aggregation in wireless sensor networks

Kwon, Soonnam; Ko, Jae Hoon; Kim, Jeongkyu; Kim, Cheeha

doi:10.1016/j.comnet.2010.09.017

Cited by 17 publications

(24 citation statements)

References 26 publications

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“…Data aggregation methods try to find and remove packets of the same event to reduce network energy consumption as well as improving the network end-to-end delay [10,11]. Generally, data aggregation methods improve their Degree of Aggregation (DOA) by imposing a predefined delay on incoming packets [13].…”

Section: Backgroundsmentioning

confidence: 99%

“…A Peony-tree-based Data Aggregation method, PDA, has been presented in [14] which has a delay bound of 15R þ D À 15 where R is the network radius and D is the maximum node degree. In most of adaptive data aggregation methods [12][13][14][15], a complicated computation should be done to determine the amount of imposed delay on incoming packets. Since this computation should be repeated throughout the whole lifetime of network, energy savings of adaptive data aggregation methods are not very remarkable.…”

Section: Backgroundsmentioning

confidence: 99%

“…In [15], amount of imposed delay on incoming packets to each sensor node is calculated in terms of last packet's arrival time. In [13], the amount of imposed delay on arrival packets is computed in terms of collected packets in each sensor node in an adaptive manner. A real-time data aggregation method that is called RDAG has been presented in [12] for real-time WSNs.…”

Section: Backgroundsmentioning

confidence: 99%

“…Several methods have been proposed to identify and remove redundant packets in order to reduce the average energy consumption in the network [10][11][12]. The basic idea behind such methods, known as Data Aggregation Methods, is the nature of many-to-one traffic pattern in WSNs [13]. Data aggregation methods reduce unnecessary packet transmissions in sensor nodes by combining and compressing redundant packets received from multiple sources into one packet [13].…”

Section: Introductionmentioning

confidence: 99%

“…The basic idea behind such methods, known as Data Aggregation Methods, is the nature of many-to-one traffic pattern in WSNs [13]. Data aggregation methods reduce unnecessary packet transmissions in sensor nodes by combining and compressing redundant packets received from multiple sources into one packet [13]. To aggregate redundant packets, sensor nodes impose delay on incoming packets to identify and remove the redundant ones.…”

Section: Introductionmentioning

confidence: 99%

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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: Backgroundsmentioning

confidence: 99%

Section: Backgroundsmentioning

confidence: 99%

Section: Backgroundsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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Impacts of wireless sensor networks strategies and topologies on prognostics and health management

et al. 2017

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In this article, we used Wireless Sensor Network (WSN) techniques for monitoring an area under consideration, in order to diagnose its state in real time. What differentiates this type of network from the traditional computer ones is that it is composed by a large number of sensor nodes having very limited and almost nonrenewable energy. A key issue in designing such networks is energy conservation because once a sensor depletes its resources, it will be dropped from the network. This will lead to coverage hole and incomplete data arriving to the sink. Therefore, preserving the energy held by the nodes so that the network keeps running for as long as possible is a very important concern. If we achieve to improve the network lifetime and Quality of Service (QoS). Diagnosing the state of area will be more accurate for a longer time. One of the most important elements to achieve a QoS in WSN is the network coverage which is usually interpreted as how well the network can observe a given area. Obviously, if the coverage decreases over time, the diagnosis quality decreases accordingly. Various coverage strategies are thus proposed by the WSN community, in order to guarantee a certain coverage rate as long as possible, to reach a certain QoS that in turn will impact the diagnosis and prognostic quality. Various other strategies are in common use in WSN like data aggregation and scheduling, to preserve a QoS in wireless sensor networks, as long as possible. We argue that such strategies are not neutral if this network is used for prognostic and health management. Some politics may have a positive impact while other ones may blur the sensed data, like data aggregation or redundancy suppression, leading to erroneous diagnostics and/or prognostics. In this work, we will show and measure the impact of each WSN strategy on the resulting estimation of diagnostics. We emphasized several issues and studied various parameters related to these strategies that have a very important impact on the network, and therefore on data diagnostics over time. To reach this goal, to evaluate both prognostic and health management with the WSN strategies, we have used six diagnostic algorithms.

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New data aggregation approach for time-constrained wireless sensor networks

Abid

Nguyen

2014

J Supercomput

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Dynamic timeout for data aggregation in wireless sensor networks

Cited by 17 publications

References 26 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

Impacts of wireless sensor networks strategies and topologies on prognostics and health management

New data aggregation approach for time-constrained wireless sensor networks

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