Evaluating the Impact of the Number of Access Points in Mobile Robots Localization Using Artificial Neural Networks

Localization is information of fundamental importance to carry out various tasks in the mobile robotic area. The exact degree of precision required in the localization depends on the nature of the task. The GPS provides global position estimation but is restricted to outdoor environments and has an inherent imprecision of a few meters. In indoor spaces, other sensors like lasers and cameras are commonly used for position estimation, but these require landmarks (or maps) in the environment and a fair amount of computation to process complex algorithms. These sensors also have a limited field of vision. Currently, Wireless Networks (WN) are widely available in indoor environments and can allow efficient global localization that requires relatively low computing resources. However, the inherent instability in the wireless signal prevents it from being used for very accurate position estimation. The growth in the number of Access Points (AP) increases the overlap signals areas and this could be a useful means of improving the precision of the localization. In this paper we evaluate the impact of the number of Access Points in mobile nodes localization using Artificial Neural Networks (ANN). We use three to eight APs as a source signal and show how the ANNs learn and generalize the data. Added to this, we evaluate the robustness of the ANNs and evaluate a heuristic to try to decrease the error in the localization. In order to validate our approach several ANNs topologies have been evaluated in experimental tests that were conducted with a mobile node in an indoor space.

UAVNet: A Mobile Wireless Mesh Network Using Unmanned Aerial Vehicles

We developed UAVNet, a framework for the autonomous deployment of a flying Wireless Mesh Network using small quadrocopter-based Unmanned Aerial Vehicles (UAVs). The flying wireless mesh nodes are automatically interconnected to each other and building an IEEE 802.11s wireless mesh network. The implemented UAVNet prototype is able to autonomously interconnect two end systems by setting up an airborne relay, consisting of one or several flying wireless mesh nodes. The developed software includes basic functionality to control the UAVs and to setup, deploy, manage, and monitor a wireless mesh network. Our evaluations have shown that UAVNet can significantly improve network performance.

Han Migration to Xinjiang Uyghur Autonomous Region: Between State Schemes and Migrants’ Strategies

Post-1949 Han migration to Xinjiang Uyghur Autonomous Region in northwest China is a hotly debated issue among the Xinjiang scholars and within the region itself. While it is often discussed using statistical data as a large-scale historical process, I argue in this article for a more differentiated view of Han migrants. I demonstrate that in the popular discourse migrants are distinguished into numerous categories like Bingtuaners , Profit-Driven Migrants, Border Supporters, Qualified Personnel, Educated Youth, and others. Accordingly, I argue that Han migrants to Xinjiang should not be understood as a homogeneous category of participants in a singular state project intended to establish state control over the region. High return rates demonstrate that state attempts to make Han settle in Xinjiang are only partly successful, and that migrants follow their own strategies when situation permits, rather than fulfill the government’s plans. Individuals who have migrated since the 1980s are especially careful in their assessment of the economic incentives of settlement, and many decide to remain mobile.