Efficient aggregation using first hop selection in WSNs

Zou, Shoudong; Nikolaidis, Ioanis; Harms, Janelle

doi:10.1504/ijsnet.2008.019252

Cited by 9 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some other factors have also been researched such as energy control in (Hua and Lau, 2006;Moh, Kim and Moh, 2006) where Moh et al present a distributed power scheduling for data aggregation, routing protocol design in (Jia, Zhao and Ma, 2008) which improves the life span of the network. In addition, maximizing the lifetime with data aggregation in wireless sensor networks have also been discussed in (Stanford and Tongngam, 2009;Li, Zhu and Chen, 2011;Zou, Nikolaidis and Harms, 2008) and efficient aggregation tree constructed in (Cheng and Yin, 2008;Chiang and Byrd, 2009;Hua and Lau, 2010) can reduce redundant data which improves the aggregation latency. (Cam, 2007) also gives a view about coding method in data aggregation and (Kafatzoglou and Papavassiliou, 2011; Solis and Obraczka, 2006) explore the trade-off for data collection when in-network aggregation is introduced.…”

Section: Related Workmentioning

confidence: 99%

Improved minimum latency aggregation scheduling in wireless sensor networks under the SINR model

Wang

Hua

et al. 2013

IJSNET

View full text Add to dashboard Cite

Wireless sensor networks are attracting much attention from the world and Minimum Latency Aggregation Scheduling(MLAS) has become one of the most significant fundamental problems in wireless sensor networks. However there are few results about efficient data aggregation algorithms under the Signal-to-Interference-plus-Noise-Ratio (SINR) model. In this paper, we propose a centralized algorithm to aggregate data from all sources in O(log 2 n) time slots where n is the total number of nodes. To the best of our knowledge, this is the current best result for the problem. This algorithm uses round scheduling, topology construction and non-linear power assignment as the main techniques. We give a detailed proof of correctness, also an aggregation latency analysis of the algorithm as well as the parameter constraints to achieve our result.

show abstract

Section: Related Workmentioning

confidence: 99%

Improved minimum latency aggregation scheduling in wireless sensor networks under the SINR model

Wang

Hua

et al. 2013

IJSNET

View full text Add to dashboard Cite

show abstract

“…In [28] Zou et al introduce the concept of flow loss multiplier, which is dependent on the spatial relationship among sensed areas, to express the impact of data aggregation on the conveyed traffic. The approach based on innetwork data aggregation at the First Hop away from each sensor data source, followed by flow-based routing of the resulting traffic, is proposed to extend the lifetime of wireless sensor networks (WSNs).…”

Section: Related Workmentioning

confidence: 99%

“…In [27] Kafetzoglou and Papavassiliou propose a framework which combines two different energy-saving methods from two different layers, application layer and Medium Access Control (MAC), by appropriately adopting sleeping mechanisms. In [28] Zou et al introduce the concept of flow loss multiplier, which is dependent on the spatial relationship among sensed areas, to express the impact of data aggregation on the conveyed traffic. The approach based on innetwork data aggregation at the First Hop away from each sensor data source, followed by flow-based routing of the resulting traffic, is proposed to extend the lifetime of wireless sensor networks (WSNs).…”

Section: Related Workmentioning

confidence: 99%

Aggregation Scheme with Secure Hierarchical Clustering for Wireless Sensor Networks

Huang

et al. 2012

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

In a large-scale wireless sensor network, a topology is needed to gather state-based data from sensor network and efficiently aggregate the data given the requirements of balanced load, minimal energy consumption, and prolonged network lifetime. In this study, we proposed a ring-based hierarchical clustering scheme (RHC) consisting of four phases: predeployment, parent-child relationship building, deployment, and member join phases. Two node types are distributed throughout the network: cluster head nodes (type 1 node) and general sensor nodes (type 2 node). The type 1 node has better battery life, software capability, and hardware features than the type 2 node; therefore, the type 1 node is a better cluster head than type 2 node. Due to our IP naming rules and type 1 nodes as cluster heads, public key cryptography, such as RSA (Rivest, Shamir, Adleman), or ECC (Elliptic Curve Cryptosystem), is easily implanted to our system to strengthen our security. The sink node is the only certification authority in our system, but n level cluster heads can be extended to n level certification authorities if needed, where n is maximum number of level.

show abstract

Routing and Aggregation Strategies for Contour Map Applications in Sensor Networks

Zou

Nikolaidis

Harms

2009

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Efficient aggregation using first hop selection in WSNs

Cited by 9 publications

References 21 publications

Improved minimum latency aggregation scheduling in wireless sensor networks under the SINR model

Improved minimum latency aggregation scheduling in wireless sensor networks under the SINR model

Aggregation Scheme with Secure Hierarchical Clustering for Wireless Sensor Networks

Routing and Aggregation Strategies for Contour Map Applications in Sensor Networks

Contact Info

Product

Resources

About