Distance Matrix Reconstruction from Incomplete Distance Information for Sensor Network Localization

This paper initiates the principled study of distance reconstruction for distance-based node localization. We address an important issue in node localization by showing that the highly incomplete set of inter-node distance measurements obtained in ad-hoc node deployments carries sufficient information for the accurate reconstruction of the missing distances, even in the presence of noise. We provide an efficient and provably accurate algorithm for this reconstruction, and we show that the resulting error is bounded, decreasing at a rate that is inversely proportional to √ n, the square root of the number of nodes in the region of deployment. Although this result is applicable to many localization schemes, in this paper we illustrate its use in conjunction with the popular MultiDimensional Scaling algorithm. Our analysis reveals valuable insights and key factors to consider during the sensor network setup phase, to improve the quality of the position estimates. 1

Distance matrix reconstruction from incomplete distance information for sensor network localization

This paper initiates the principled study of distance reconstruction for distance-based node localization. We address an important issue in node localization by showing that the highly incomplete set of inter-node distance measurements obtained in ad-hoc node deployments carries sufficient information for the accurate reconstruction of the missing distances, even in the presence of noise. We provide an efficient and provably accurate algorithm for this reconstruction, and we show that the resulting error is bounded, decreasing at a rate that is inversely proportional to √ n, the square root of the number of nodes in the region of deployment. Although this result is applicable to many localization schemes, in this paper we illustrate its use in conjunction with the popular MultiDimensional Scaling algorithm. Our analysis reveals valuable insights and key factors to consider during the sensor network setup phase, to improve the quality of the position estimates. 1

Time, Seasonality, and Trade: Swedish/Finnish-Sámi Interactions in Early Modern Lapland

Recent theoretical debates have identified time as a key area for research by historical archaeologists. In this paper we present evidence from Tornio, in northern Finland, and suggest that the early-17th-century colonists who founded this town developed a multidimensional conception of time that varied according to context and allowed deeply held folk beliefs to coexist alongside Lutheran doctrines and also facilitated seasonal trade with the indigenous Sámi people in the upper reaches of Lapland