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Open AccessJournal ArticleDOI

Data aggregation and routing in Wireless Sensor Networks: Optimal and heuristic algorithms

Jamal N. Al-Karaki, +2 more
- 01 May 2009 - 
- Vol. 53, Iss: 7, pp 945-960
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TLDR
This paper presents Grid-based Routing and Aggregator Selection Scheme (GRASS), a scheme for WSNs that can achieve low energy dissipation and low latency without sacrificing quality, and shows that, when compared to other schemes, GRASS improves system lifetime with acceptable levels of latency in data aggregation and without sacrificing data quality.
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This article is published in Computer Networks.The article was published on 2009-05-01 and is currently open access. It has received 163 citations till now. The article focuses on the topics: Static routing & Wireless sensor network.

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Citations
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Journal ArticleDOI

Cluster head selection for energy efficient and delay-less routing in wireless sensor network

TL;DR: Firefly with cyclic randomization is proposed for selecting the best cluster head for wireless sensor network and the network performance is increased in this method when compared to the other conventional algorithms.
Journal ArticleDOI

Review: A survey on cross-layer solutions for wireless sensor networks

TL;DR: This survey proposes to identify the key problems of WSNs and gather available cross-layer solutions for them that have been proposed so far, in order to provide insights on the identification of open issues and provide guidelines for future proposals.
Journal ArticleDOI

Path-Loss Modeling for Wireless Sensor Networks: A review of models and comparative evaluations.

TL;DR: An overview of the experimentally verified propagation models for WSNs is presented, and quantitative comparisons of propagation models employed in WSN research under various scenarios and frequency bands are provided.
Journal ArticleDOI

Data Density Correlation Degree Clustering Method for Data Aggregation in WSN

TL;DR: The data density correlation degree is a spatial correlation measurement that measures the correlation between a sensor node's data and its neighboring sensor nodes' data and the shape of clusters obtained by DDCD clustering method can be adapted to the environment.
Journal ArticleDOI

Energy-efficient node scheduling algorithms for wireless sensor networks using Markov Random Field model

TL;DR: This paper introduces how to model the spatial correlation among sensed data by Markov Random Field model and proposes a novel Data Amendment Procedure (DAP), Representative node Selection Procedure (RSP) and energy-efficient Node Scheduling Algorithm (NSA) respectively for these above problems.
References
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Book

Genetic algorithms in search, optimization, and machine learning

TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Book

Computers and Intractability: A Guide to the Theory of NP-Completeness

TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
Journal ArticleDOI

Wireless sensor networks: a survey

TL;DR: The concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics is described.
Book

Genetic Algorithms

Related Papers (5)
Frequently Asked Questions (16)
Q1. Why is an energy loss model used for relatively short distances?

Due to attenuation with distance, an energy loss model with d2ij is used for relatively short distances, where dij is the distance between sensor nodes i and j. 

In this paper, the authors present solutions for the data gathering and routing problem with in-network aggregation in WSNs. The authors present Grid-based Routing and Aggregator Selection Scheme ( GRASS ), a scheme for WSNs that can achieve low energy dissipation and low latency without sacrificing quality. 

In particular, the authors developed GRASS ( Grid-based Routing and Aggregator Selection Scheme ), a scheme for WSNs that combines the ideas of fixed cluster-based routing together with application-specific data aggregation in order to enhance the wireless sensor network performance in terms of extending the network lifetime, while incurring acceptable levels of latency under data aggregation. 

in order to speed up the execution of the ILP solver, the authors limited the search space of the number of MAs to be about one half of the total number of LAs, which is the maximum number of second level aggregators under the two level aggregation assumption. 

Whenever a node is inserted in the new-candidate-list, CBAH keeps a pointer to the predecessor node, which is required to backtrack the route(s) to the source node in case this node falls on the selected route. 

This is because in DD data forwarding paths from different sources may cross or overlap with each other anywhere in the network area, thus there is more interference when the number of sources is large, whereas in their scheme each LA source node sends data on the virtual grid, thus data flows on the grid faster and the routes are selected to balance power consumption in the network. 

The power consumption at each node is also dependent on the determination of the actual routing of the data traffic, i.e., the authors must find the power consumed by each LA source node when participating in routing data over G. 

Since with multiple levels of aggregations the maximum number of MAs is n − 1, where n is the total number of LAs, the authors did not impose any upper bound on the number of MAs, as this will not provide any significant reduction in the search space. 

After finding the set of routes for each source s in a group g (routes[g][s]) and in order to determine what route among these routes the source s will use, CBAH invokes a function called select-route(.), and described in Figure 5. 

The importance of the candidate-list is that it allows one to quickly select the next LA node needed to expand the route search for a certain source node. 

the energy consumed by a sensor node i in receiving a 1000-bit data packet is 1000*50 nJ/bit= 50µJ, while the energy consumed in transmitting a data packet from sensor i to sensor j is given by T = 50µJ + 100nJ/m2 × d2ij . 

Their results show that, when compared to other approaches in the literature, the proposed scheme is able to improve the network lifetime while incurring acceptable levels of latency and without sacrificing quality. 

The following two constraints find the maximum power consumption over all LA nodes, while respecting the maximum power consumption limit of any LA node,ρ ≥ Pi/Ei , ∀ i (19)ρ ≤ 1/Tmin (20)The following constraint ensures that the power consumed at each LA node is sufficient to send the amount of required traffic to the other nodes. 

Effect of number of MAs: the authors study the effect of varying the number of MAs, M , for a fixed value of n on the increase in the network lifetime in the 2L aggregation scheme. 

If data gathering and reporting delay is not a concern, multi-level aggregation schemes would be a good choice in this case as these schemes consume less power and hence allow for longer network lifetime. 

The heuristic CBAH will find routes on the graph G for each source node of each group such that power consumption in the network is balanced.