Probability Based Dynamic Load-Balancing Tree Algorithm for Wireless Sensor Networks

Yan, Tingxin; Bi, Yanzhong; Sun, Limin; Zhu, Hongsong

doi:10.1007/11534310_72

Cited by 20 publications

(19 citation statements)

References 8 publications

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“…The MD-tree can increase the amount of in-network processing by constructing the tree in such a way that messages generated from sensor nodes can be merged more often and earlier, thus minimizing the energy consumption. In [19], a query routing trees are formed by balancing the data load to be transmitted from one tree level to the next.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Dynamic Query Routing Tree Algorithm for Wireless Sensor Networks

Kim¹,

Park²

2012

IJACSA

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Abstract-To exploit in answering queries generated by the sink for the sensor networks, we propose an efficient routing protocol called energy-efficient dynamic routing tree (EDRT) algorithm. The idea of EDRT is to maximize in-network processing opportunities using the parent nodes and sibling nodes. Innetwork processing reduces the number of message transmission by partially aggregating results of an aggregate query in intermediate nodes, or merging the results in one message. This results in reduction of communication cost. Our experimental results based on simulations prove that our proposed method can reduce message transmissions more than query specific routing tree (QSRT) and flooding-based routing tree (FRT).

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Section: Related Workmentioning

confidence: 99%

Energy-Efficient Dynamic Query Routing Tree Algorithm for Wireless Sensor Networks

Kim¹,

Park²

2012

IJACSA

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“…Although similar models for the network structure have been considered in previous works [4,5,6,7], we formalize the network modeling and also account for key practical considerations excluded in other work, such as asymmetric links and multiple sink nodes in the network. Further, our DAG modeling is only the basis for our state-based routing approach and our data gathering protocols, which are our major contributions.…”

Section: Motivation and Problem Statementmentioning

confidence: 99%

“…al too propose a dynamic load-balanced tree algorithm, DLBT [5], for data gathering in a single-sink data-centric WSN that defines a network modeling similar to our shortest-path DAG model and constructs a dynamic load tree with forwarding probabilities on the edges. The forwarding probabilities are computed such that they are inversely proportional to the expected load on the nodes.…”

Section: Related Workmentioning

confidence: 99%

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Dynamic state-based routing for load balancing and efficient data gathering in wireless sensor networks

Ranganathan

Berman

2010

2010 International Symposium on Collaborative Technologies and Systems

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Data gathering is a fundamental operation in wireless sensor networks. For the online data gathering problem, we consider the key issues of balancing the load on the nodes to achieve longer network lifetime, and that of balancing the load on the network links to achieve greater reliability in the network. We model the given network as a shortest-distance DAG D, which defines a set of parent nodes for each node that determine the minimum-hops paths from the node to a sink. Data gathering in D is accomplished using a dynamic routing approach, where each node selects a parent using a parent selection function σ to forward the sensed data, which generates a dynamic forest (D, σ) in the network. We investigate a dynamic state-based routing approach where σ is defined using the current state of the network. Based on our earlier work [1], we propose two dynamic state-based routing algorithms -MPE Routing and WPE Routing, that aim to load-balance the nodes as well as the edges of D in order to achieve longer network lifetime as well as greater disjointness. We evaluate the performance of our algorithms with respect to the three goodness measures -network lifetime, nodal load-balancing and disjointness, and compare it with two benchmark algorithms as well as existing data gathering schemes. Our simulation results show that our algorithms perform consistently better with respect to our goodness measures than the benchmark algorithms and other approaches.

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“…A distributed algorithm to create a query routing tree without the assumption that every node can transmit directly to the sink, has been proposed in [26]. Their algorithm balances the data load to be transmitted from one tree level to the next.…”

Section: Related Workmentioning

confidence: 99%

Minimum-hot-spot query trees for wireless sensor networks

Chatzimilioudis

Zeinalipour-Yazti

Gunopulos

2010

Proceedings of the Ninth ACM International Workshop on Data Engineering for Wireless and Mobile Access

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We propose a distributed algorithm to construct a balanced communication tree that serves in gathering data from the network nodes to a sink. Our algorithm constructs a near-optimally balanced communication tree with minimum overhead. The balancing of the node degrees results in the minimization of packet collisions during query execution, that would otherwise require numerous retransmissions and reduce the lifetime of the network. We compare our simple distributed algorithm against previous work and a centralized solution and show that for most network layouts it outperforms competition and achieves tree balance very close to the centralized algorithm. It also has the smallest energy overhead possible to construct the tree, increasing the lifetime of the network even more.

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Probability Based Dynamic Load-Balancing Tree Algorithm for Wireless Sensor Networks

Cited by 20 publications

References 8 publications

Energy-Efficient Dynamic Query Routing Tree Algorithm for Wireless Sensor Networks

Energy-Efficient Dynamic Query Routing Tree Algorithm for Wireless Sensor Networks

Dynamic state-based routing for load balancing and efficient data gathering in wireless sensor networks

Minimum-hot-spot query trees for wireless sensor networks

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