David Akopian

This work addresses mobile computations related to integrated inertial sensors for activity monitoring, such as accelerometers, gyroscopes, integrated global positioning system (GPS) and WLAN-based positioning, that can be used for activity monitoring. Some of the aspects will be discussed in this paper. Each of the sensing data sources has its own characteristics such as specific data formats, data rates, signal acquisition durations etc., and these specifications affect energy consumption… Show more

This work addresses mobile computations related to integrated inertial sensors for activity monitoring, such as accelerometers, gyroscopes, integrated global positioning system (GPS) and WLAN-based positioning, that can be used for activity monitoring. Some of the aspects will be discussed in this paper. Each of the sensing data sources has its own characteristics such as specific data formats, data rates, signal acquisition durations etc., and these specifications affect energy consumption. Energy consumption significantly varies as sensor data acquisition is followed by data analysis including various transformations and signal processing algorithms. This paper will address several aspects of more optimal activity monitoring implementations exploiting state-of-the-art capabilities of modern platforms. Show less

This work addresses mobile computations related to integrated inertial sensors for activity monitoring, such as accelerometers, gyroscopes, integrated global positioning system (GPS) and WLAN-based positioning, that can be used for activity monitoring. Some of the aspects will be discussed in this paper. Each of the sensing data sources has its own characteristics such as specific data formats, data rates, signal acquisition durations etc., and these specifications affect energy consumption… Show more

This work addresses mobile computations related to integrated inertial sensors for activity monitoring, such as accelerometers, gyroscopes, integrated global positioning system (GPS) and WLAN-based positioning, that can be used for activity monitoring. Some of the aspects will be discussed in this paper. Each of the sensing data sources has its own characteristics such as specific data formats, data rates, signal acquisition durations etc., and these specifications affect energy consumption. Energy consumption significantly varies as sensor data acquisition is followed by data analysis including various transformations and signal processing algorithms. This paper will address several aspects of more optimal activity monitoring implementations exploiting state-of-the-art capabilities of modern platforms. Show less

Currently, conventional Global Positioning System (GPS) receivers work well in open-sky environments. However, location finding in weak-signal conditions, such as urban canyons and indoors, is challenging and has been the subject of extensive research. Assisted GPS (A-GPS) is one of the concepts that helps receivers acquire weak signals by receiving assistance data from wireless networks, such as orbital parameters, and coarse time and location references. A-GPS also improves time-to-first-fix… Show more

Currently, conventional Global Positioning System (GPS) receivers work well in open-sky environments. However, location finding in weak-signal conditions, such as urban canyons and indoors, is challenging and has been the subject of extensive research. Assisted GPS (A-GPS) is one of the concepts that helps receivers acquire weak signals by receiving assistance data from wireless networks, such as orbital parameters, and coarse time and location references. A-GPS also improves time-to-first-fix (TTFF) through faster delivery of information that is recovered from navigation data broadcast by GPS satellites. Receiver technology developers rely on signal simulators for testing real-world scenarios. While A-GPS support has been integrated in many simulators, probabilistic models of delays occurring during assistance data delivery have not been studied properly. This paper provides a methodology of A-GPS network delay modeling that is applicable to various simulation environments. Particularly, a testbed is designed to collect delay data, model them statistically and integrate the model in a simulator. The testbed employs secure user plane location (SUPL) architecture in two modes, mobile station (MS) based (MS-based) and MS-assisted, where data channels are used to communicate A-GPS assistance data.Measurement campaigns are conducted and network delay models are derived for various representative distances between assistance servers and receivers, and for various networks that users connect to, such as LAN, WLAN, third-generation mobile telecommunication (3G), high-speed downlink packet access (HSDPA), and fourth-generation long term evolution (4G LTE), with the Transmission Control Protocol and the Internet Protocol (TCP/IP) connection between a server and a receiver Show less

The PreNotiS (preventive notification system) was proposed to address the current lack in consumer prevention and disaster informatics systems. The underscore of this letter is to propose PreNotiS as a provision of trusted proxies of information sourcing to be integral to the disaster informatics framework. To promote loose coupling among subsystems, PreNotiS has evolved into a model-view-controller (MVC) architecture via object-oriented incremental prototyping. The MVC specifies how all… Show more

The PreNotiS (preventive notification system) was proposed to address the current lack in consumer prevention and disaster informatics systems. The underscore of this letter is to propose PreNotiS as a provision of trusted proxies of information sourcing to be integral to the disaster informatics framework. To promote loose coupling among subsystems, PreNotiS has evolved into a model-view-controller (MVC) architecture via object-oriented incremental prototyping. The MVC specifies how all subsystems and how they interact with each other. A testing framework is also proposed for the PreNotiS to verify multiple concurrent user access which might be observable during disasters. The framework relies on conceptually similar self-test modules to help with serviceability. Show less