Weighted Centroid Localization in Zigbee-based Sensor Networks

Blumenthal, J.; Grossmann, R.; Golatowski, Frank; Timmermann, Dirk

doi:10.1109/wisp.2007.4447528

Cited by 415 publications

(297 citation statements)

References 3 publications

Supporting

Mentioning

289

Contrasting

Unclassified

Order By: Relevance

“…Publicized algorithms of the centroid location methods use the centroid to calculate the position of an MS (Blumenthal et al, 2007;Schuhmann et al, 2010;Bulusu et al, 2000). For example, an MS receives signals from multi-cells at the position M (x,y) and the cells belong to BSs with positions of B i (x,y) (i=1,2,…,n.…”

Section: Methodsmentioning

confidence: 99%

“…A centroid location method only uses the average of the coordinates of BSs to approximate the location of an MS. A WCL (Blumenthal et al, 2007) uses the weighted coefficients to ensure an improved localization which is formulated by Eq. 3: …”

Section: Methodsmentioning

confidence: 99%

“…The weighted centroid-based localization algorithm is a good backup approach because of its simplicity and efficiency, on the recent advancement of this class of positioning methods, one can refer to (Blumenthal et al, 2007) for the Weighted Centroid Localization (WCL), (Behnke and Timmermann, 2008) for the adaptive WCL (AWCL), (Schuhmann et al, 2010) for the improved WCL (IWCL) and so on.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Weighted Centroid Correction Localization in Cellular Systems

Li¹

2011

American Journal of Engineering and Applied Sciences

View full text Add to dashboard Cite

Problem statement:There is a large demand for wireless Location-Based Service (LBS) and it is provided by many wireless cellular systems. In process of positioning a Mobile Station (MS), the computing speed is as important as the positioning accuracy and the algorithm should also be resistant to environmental influences. Approach: A new positioning method based on Weighted Centroid Correction Localization (WCCL) for wireless cellular systems is introduced in this article. Firstly, referring to the receiving-state of an MS in cellular systems, it computes a weighted centroid of surrounding Base Stations (BSs) as a rough approximate position of the MS. Then, according to the distances between the MS and the BSs being less or bigger than the computed distances between the BSs and the weighted centroid, it corrects the coordinate of the weighted centroid towards the directions of the BSs by moving it closer or farther in turn. Results: According to our experiments, WCCL improves the positioning accuracy, as well as to provide a better resistance to environmental influences. Conclusion: As a modified centroid-based localization algorithm, WCCL obtains weighting factors from the receiving-state of MS in multi-cells structured cellular systems and obtains a better positioning result in cellular systems without updating the network equipment. Therefore, for the cellular positioning problem, WCCL algorithm can be an alternate solution.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Weighted Centroid Correction Localization in Cellular Systems

Li¹

2011

American Journal of Engineering and Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Weighted Centroid Localization [17] adds different contributions to the involved node coordinate information in estimating the location of the target node. We usually call the contribution as weight.…”

Section: B Weighted Centroid Localizationmentioning

confidence: 99%

An Algorithm for Jammer Localization in Wireless Sensor Networks

Cheng

Zhu

2012

2012 IEEE 26th International Conference on Advanced Information Networking and Applications

View full text Add to dashboard Cite

Abstract-In wireless sensor networks (WSNs), jamming attacks have become a great concern recently. Finding the location of a jamming device is important so as to take security actions against the jammer and restore the network communication. In this paper, we take a comprehensive study on the jammer localization problem, and propose a simple while effective algorithm called Double Circle Localization (DCL). DCL is based on minimum bounding circle (MBC) and maximum inscribed circle (MIC). We implement and evaluate DCL under different conditions, including different node densities, jammer's transmission powers and antenna orientations, and compare it with three existing jammer localization algorithms through both simulation and experiments. Our evaluation results have demonstrated that, compared with all other approaches, DCL achieves the best accuracy in jammer localization.

show abstract

“…The wireless sensor node is the Tmote Sky made by the Moteiv, including TI MSP430 processor, CC2420 RF chip and F type antenna. Its module communicating range is that its indoor is 50 meters and its outdoor is 125 meter [6]. TI MSP430 processor has many advantages, such as many kinds of power-saving modes, the function of being waking up quickly (< 6µs), the FLASH internal memory with the 48KB+256KB, many simulations, digital input&output connection tubs and others.…”

Section: Hard Wares Introductionmentioning

confidence: 99%

Research on RSSI–Based Localization System in the Wireless Sensor Network

Liu¹

2014

TOAUTOCJ

View full text Add to dashboard Cite

Due to the rapid development in wireless sensor networks, the applications of the WSN have been extended to different environment and achieved a more diversified purpose. AS for the localization system, there are many related research achievements. A set of localization system is built through the system integration between the small-scale wireless hardware sensor and the wireless sensor platform, through the use of the RSSI and the integration of the fuzzy inference system. The blind nodes are experimented in the indoor environment so that the system's efficiency can be verified. The system is implemented in the 8 meters multiplying 8 meters indoor space which is divided into 16 sections. Then 4 anchor nodes are placed in the corners and the fuzzy identification system is built for the nodes' RSSI signal in the blocks. Later, the localization test is implemented in the random positions within the blocks. The experimental results show that the localization system accuracy rate is more than 80% and can successfully reach the preliminary localization system.

show abstract

Weighted Centroid Localization in Zigbee-based Sensor Networks

Cited by 415 publications

References 3 publications

Weighted Centroid Correction Localization in Cellular Systems

Weighted Centroid Correction Localization in Cellular Systems

An Algorithm for Jammer Localization in Wireless Sensor Networks

Research on RSSI–Based Localization System in the Wireless Sensor Network

Contact Info

Product

Resources

About