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Comparison of 2.4 GHz WiFi FTM- and RSSI-Based Indoor Positioning Methods in Realistic Scenarios
(2020)
In this work we present a method to estimate an indoor position with the help of smartphone sensors and without any knowledge of absolute positioning systems like Wi-Fi signals. Our
system uses particle filtering to solve the recursive state estimation problem of finding the position of a pedestrian. We show how to integrate the information of the previous state into the weight update step and how the observation data can help within the state transition model. High positional accuracy can be achieved by only knowing that the pedestrian makes a foot step or changes her direction together with floor map information.
The Smartphone-Based Offline Indoor Location Competition at IPIN 2016: Analysis and Future Work
(2017)
While connection speeds are increasing slowly, some ISPs mention plans about possible traffic limitations in the near future which would keep internet traffic expensive. In addition, Green IT became more important especially over the last few years. Besides on-demand content like video live streams, HTTP traffic plays an important role. Thinking of textual web content, compression quickly comes to mind as a possibility to reduce traffic. The current HTTP/1.1 standard only provides gzip as an option for content encoding. HTTP/2.0 is under heavy development and numerous new algorithms have been established over the last few years. This paper analyzes HTTP traffic composition on a production server and concludes that about 50% is compressible. It further examines the effectiveness of custom and existing algorithms with
regards to compression ratio, speed, and energy consumption. Our results show that gzip is a sound choice for web traffic but alternatives like LZ4 are faster and provide competitive compression ratios.