Achievable Error Bounds on Localization with Wireless Sensor Networks

Chan, Yiu Wing Edwin; Soong, Boon‐Hee

doi:10.1109/wicom.2009.5303139

Cited by 4 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed dWCL generalizes weighted centroid localization algorithms. It should be highlighted that the CL algorithm [19] is in fact a special case of dWCL with M = 1, and its performance analysis can be found in the authors' earlier work [30].…”

Section: Relation To Other Wcl Algorithmsmentioning

confidence: 99%

“…In a related work [29], U (γ ) represents the percentage of area within D that has a RSS equal to or greater than a threshold γ . In a similar fashion and based on [30], U i is defined as the percentage of area within D that has a RSS equal to or greater than a threshold γ i , and it has the following form:…”

Section: A Coverage Areamentioning

confidence: 99%

“…Following [30], the expectation of x-coordinates of sensor nodes of RSS level i, µ i , can be shown to be equal to…”

Section: B Accuracy and Precisionmentioning

confidence: 99%

“…Alternatively, one can also use the optimization formulation (P1) in (30) to obtain the w * . Since the objective function of (P1) is nonlinear and nonconvex in general (as indicated in Fig.…”

Section: F Converging To Other Wcl Algorithmsmentioning

confidence: 99%

See 3 more Smart Citations

Discrete Weighted Centroid Localization (dWCL): Performance Analysis and Optimization

Chan

Soong

2016

IEEE Access

Self Cite

View full text Add to dashboard Cite

In this paper, a discrete version of weighted centroid localization algorithm (dWCL) is proposed for a wireless-sensor-network-based indoor positioning system. Under this architecture, a wireless device can estimate its own position based on the location information of connected wireless sensor nodes in the network. Unlike other weighted centroid localization algorithms, dWCL classifies the sensor nodes into a number of groups and assigns them weights taken from a set of discrete values. With the weighting function and the range information decoupled, the dWCL framework is flexible to adjust its performance in accordance with the requirements. Its capability to generalize some existing weighted centroid localization algorithms is also demonstrated. In addition, the performance of dWCL is characterized by the error distribution in terms of accuracy and precision. Further enhancement is achieved by optimizing the system parameters. Finally, the simulation results are presented to validate the proposed scheme and analytical framework.

show abstract

Section: Relation To Other Wcl Algorithmsmentioning

confidence: 99%

Section: A Coverage Areamentioning

confidence: 99%

“…Following [30], the expectation of x-coordinates of sensor nodes of RSS level i, µ i , can be shown to be equal to…”

Section: B Accuracy and Precisionmentioning

confidence: 99%

Section: F Converging To Other Wcl Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

Discrete Weighted Centroid Localization (dWCL): Performance Analysis and Optimization

Chan

Soong

2016

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

A New Lower Bound on Range-Free Localization Algorithms in Wireless Sensor Networks

Chan

Soong

2011

IEEE Commun. Lett.

View full text Add to dashboard Cite

Cascaded Simple Filters for Accurate and Lightweight Email-Spam Detection

Takesue

2010

2010 Fourth International Conference on Emerging Security Information, Systems and Technologies

View full text Add to dashboard Cite

Accurate spam filters, such as the Bayesian filter, need a large cost for off-line training (or learning) based on the analysis of a large corpus of email. This paper presents cascaded simple, i.e., rule-based, filters for accurate and lightweight detection of email spam. We cascade three filters that classify email based on respectively the fingerprints of message bodies, the white and black lists of email addresses in the From header, and the words specific to spam and legitimate email in the Subject header. Our filters need no training, but collect by themselves the information above when they are working, and especially when the user notifies them of their false negative decision (classifying spam as legitimate). We show by experiment with about 20,000 real world emails that the cascaded simple filters achieve the false negative rate of about 0.025 with no false positive (deciding legal email as spam) and the high performance of about 90 emails per second.

show abstract

Achievable Error Bounds on Localization with Wireless Sensor Networks

Cited by 4 publications

References 18 publications

Discrete Weighted Centroid Localization (dWCL): Performance Analysis and Optimization

Discrete Weighted Centroid Localization (dWCL): Performance Analysis and Optimization

A New Lower Bound on Range-Free Localization Algorithms in Wireless Sensor Networks

Cascaded Simple Filters for Accurate and Lightweight Email-Spam Detection

Contact Info

Product

Resources

About