System architecture directions for networked sensors
ASPLOS IX Proceedings of the ninth international conference on Architectural support for programming languages and operating systems
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Localization from mere connectivity
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
On the influence of the kernel on the consistency of support vector machines
The Journal of Machine Learning Research
Convex Optimization
A kernel-based learning approach to ad hoc sensor network localization
ACM Transactions on Sensor Networks (TOSN)
Second-Order Cone Programming Relaxation of Sensor Network Localization
SIAM Journal on Optimization
Using tracked mobile sensors to make maps of environmental effects
Personal and Ubiquitous Computing - Special Issue: Implications of the socio-physical contexts when interacting with mobile media
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Wireless sensor location estimation is an important area and attracts considerable research interests. In this paper, we present a novel graph embedding method for the localization problem by using signal strengths. We view the wireless sensor nodes as a group of distributed devices, and employ an appropriate kernel function to measure the similarity between sensors. The kernel function can be naturally defined according to the signal strength matrix. Then we formulate the localization problem as a graph embedding problem. Finally, we use the kernel locality preserving projection (KLPP) technique to estimate the relative locations of all sensor nodes. Given sufficient number of anchors, the relative locations can be transformed into physical locations. The main advantage of formulating the localization problem as graph embedding problem is that it allows us to construct a graph to preserve the topological structure of the sensor networks. We evaluate our method based on various network topologies, and analyze its performance. We also compare our method with several existing methods, and demonstrate the high efficiency of our proposed method.