Although mobile health monitoring where mobile sensors continuously gather, process, and update sensor readings (e.g. vital signals) from patient's sensors is emerging, little effort has been investigated in an energy-efficient management of sensor information gathering and processing. Mobile health monitoring with the focus of energy consumption may instead be holistically analyzed and systematically designed as a global solution to optimization subproblems. This paper presents an attempt to decompose the very complex mobile health monitoring system whose layer in the system corresponds to decomposed subproblems, and interfaces between them are quantified as functions of the optimization variables in order to orchestrate the subproblems. We propose a distributed and energy-saving mobile health platform, called mHealthMon where mobile users publish/access sensor data via a cloud computing-based distributed P2P overlay network. The key objective is to satisfy the mobile health monitoring application's quality of service requirements by modeling each subsystem: mobile clients with medical sensors, wireless network medium, and distributed cloud services. By simulations based on experimental data, we present the proposed system can achieve up to 10.1 times more energy-efficient and 20.2 times faster compared to a standalone mobile health monitoring application, in various mobile health monitoring scenarios applying a realistic mobility model.
Urban sensing where mobile users continuously gather, process, and share location-sensitive sensor data (e.g., street images, road condition, traffic flow) is emerging as a new network paradigm of sensor information sharing in urban environments. The key enablers are the smartphones (e.g., iPhones and Android phones) equipped with onboard sensors (e.g., cameras, accelerometer, compass, GPS) and various wireless devices (e.g., WiFi and 2/3G). The goal of this paper is to design a scalable sensor networking platform where millions of users on the move can participate in urban sensing and share locationaware information using always-on cellular data connections. We propose a two-tier sensor networking platform called GeoServ where mobile users publish/access sensor data via an Internetbased distributed P2P overlay network. The main contribution of this paper is two-fold: a location-aware sensor data retrieval scheme that supports geographic range queries, and a locationaware publish-subscribe scheme that enables efficient multicast routing over a group of subscribed users. We prove that GeoServ protocols preserve locality and validate their performance via extensive simulations.
This paper addresses the energy attacks towards wireless systems, where energy is the most critical constraint to lifetime and reliability. We for the first time propose a hardwarebased energy attack, namely energy hardware Trojans (HTs), which can be well hidden in the wireless systems and trigger ultra-high energy increases at runtime. Then, we develop a non-destructive HT detection approach to identify the energy attack by remotely sampling the power profiles of the system and characterizing the gate-level temperatures. Our evaluation results on ISCAS benchmarks indicate the effectiveness of the proposed energy attacks and defense techniques.
We observe that the recent advances in big data computing have empowered model-based services such as speech recognition, face recognition, context-aware service, and many other services. Various sources of user's logs can be utilized in remodeling or adapting existing models to improve the quality of service. We propose a system that can support store/retrieve data and process them in a scalable manner. Recently advances in ASR and big data technologies drive more personalized services in many areas of services. A speaker adaptation is one good example which requires huge computation cost in creating a personalized acoustic model and corresponding language model over 100s millions of Samsung product users. We propose a personalized and scalable ASR system powered by the big data infrastructure which brings data-driven personalized opportunities to voiceenabled services such as voice-to-text transcriber, voice-enabled web search in a peta bytes scale. We verify the feasibility of speaker adaptation based on 107 testers' recordings and obtain about 10% of recognition accuracy. We study an optimal set of execution environments by executing jobs running either on Hadoop 1 or Hadoop 2 cluster, and move forward performance optimization strategies: workflow compaction, file compression, best file system selection among several distributed file systems. We devise a metric for the cost of personalized model creation to compare the efficiency of one cluster with the other cluster, and it provides the estimated total execution time for the given number of machines. We finally introduce our in-house object storage and data storage design, and their high performance compared to state-of-the art systems, optimized for voiceenabled services to effectively support small and large files.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.