Google Tech Talk September 20, 2010 Presented by Prof. Moustafa Youssef. ABSTRACT In this talk, we present our recent results on using the internal sensors available on the Android platform for enhancing both the accuracy and energy-efficiency of the Android location provider. In the first part of the talk, we present CellSense, a probabilistic RSSI-based fingerprinting location determination system for GSM phones. We discuss the challenges of implementing a probabilistic fingerprinting localization technique in GSM networks and present the details of the CellSense system and how it addresses the challenges. Results for two different testbeds, representing urban and rural environments, show that CellSense provides at least 23.8% enhancement in accuracy in rural areas and at least 86.4% in urban areas compared to other RSSI-based GSM localization systems. This comes with a minimal increase in computational requirements. In the second part of the talk, introduce a low-energy calibration-free localization scheme based on the available internal sensors in many of today's phones. We start by energy profiling the different sensors that can be used for localization. Based on that, we propose GAC: a hybrid GPS/accelerometer/compass scheme that depends mainly on using the low-energy accelerometer and compass sensors and uses the GPS infrequently for synchronization. We implemented our system on Android-enabled cell phones and evaluated it in both highways and intra-city driving ...
Saturday, February 9, 2013
Locationoid: An Accurate Energy-Efficient Location Provider for the Android Platform
Locationoid: An Accurate Energy-Efficient Location Provider for the Android Platform Tube. Duration : 56.90 Mins.
Google Tech Talk September 20, 2010 Presented by Prof. Moustafa Youssef. ABSTRACT In this talk, we present our recent results on using the internal sensors available on the Android platform for enhancing both the accuracy and energy-efficiency of the Android location provider. In the first part of the talk, we present CellSense, a probabilistic RSSI-based fingerprinting location determination system for GSM phones. We discuss the challenges of implementing a probabilistic fingerprinting localization technique in GSM networks and present the details of the CellSense system and how it addresses the challenges. Results for two different testbeds, representing urban and rural environments, show that CellSense provides at least 23.8% enhancement in accuracy in rural areas and at least 86.4% in urban areas compared to other RSSI-based GSM localization systems. This comes with a minimal increase in computational requirements. In the second part of the talk, introduce a low-energy calibration-free localization scheme based on the available internal sensors in many of today's phones. We start by energy profiling the different sensors that can be used for localization. Based on that, we propose GAC: a hybrid GPS/accelerometer/compass scheme that depends mainly on using the low-energy accelerometer and compass sensors and uses the GPS infrequently for synchronization. We implemented our system on Android-enabled cell phones and evaluated it in both highways and intra-city driving ...
Google Tech Talk September 20, 2010 Presented by Prof. Moustafa Youssef. ABSTRACT In this talk, we present our recent results on using the internal sensors available on the Android platform for enhancing both the accuracy and energy-efficiency of the Android location provider. In the first part of the talk, we present CellSense, a probabilistic RSSI-based fingerprinting location determination system for GSM phones. We discuss the challenges of implementing a probabilistic fingerprinting localization technique in GSM networks and present the details of the CellSense system and how it addresses the challenges. Results for two different testbeds, representing urban and rural environments, show that CellSense provides at least 23.8% enhancement in accuracy in rural areas and at least 86.4% in urban areas compared to other RSSI-based GSM localization systems. This comes with a minimal increase in computational requirements. In the second part of the talk, introduce a low-energy calibration-free localization scheme based on the available internal sensors in many of today's phones. We start by energy profiling the different sensors that can be used for localization. Based on that, we propose GAC: a hybrid GPS/accelerometer/compass scheme that depends mainly on using the low-energy accelerometer and compass sensors and uses the GPS infrequently for synchronization. We implemented our system on Android-enabled cell phones and evaluated it in both highways and intra-city driving ...
Subscribe to:
Post Comments (Atom)
No comments:
Post a Comment