We consider rescue missions in postdisaster scenarios with particularly difficult environments where no infrastructure is available. Given the increasing popularity of smartphones and wearable devices, this paper proposes a rescue system which uses the Doppler effect to determine the direction of Wi-Fi signals emitted from disaster survivors' mobile devices to help rescuers quickly locate the survivors. First, we investigate the impact of the search and rescue environment on the direction-finding accuracy of Doppler effect to identify the major challenge and several implementation issues of the system. Then, to address the major challenge of Doppler shifts being too small, we propose an algorithm, which consists of three mechanisms, to solve the problem with the objective of maximizing the direction-finding accuracy. These mechanisms improve the direction-finding accuracy via eliminating the frequency fluctuation as much as possible and improving the sensitivity on small frequency shifts. Also, an active detection scheme is proposed to ensure that the survivors' devices emit steady and continuous Wi-Fi signals, along with a decision logic to minimize energy consumption by the active scheme. We implement the rescue system as a mobile application on Android smartphones and conduct extensive experiments in real-world environments. Results show that the proposed system can reduce rescue times by up to half while consuming reasonable amounts of energy from survivor smartphones.
Self-powered intermittent systems featuring nonvolatile processors (NVPs) allow for accumulative execution in unstable power environments. However, frequent power failures may cause incorrect NVP execution results due to invalid data generated intermittently. This paper presents a HW/SW co-design, called HomeRun, to guarantee atomicity by ensuring that an uninterruptible program section can be run through at one execution. We design a HW module to ensure that a power pulse is sufficient for an atomic section, and develop a SW mechanism for programmers to protect atomic sections. The proposed design is validated through the development of a prototype pattern locking system. Experimental results demonstrate that the proposed design can completely guarantee atomicity and significantly improve the energy utilization of self-powered intermittent systems.
The increasingly high display resolution of mobile devices imposes a further burden on energy consumption. Existing schemes manage either OLED or GPU power to save energy. This paper presents the design, algorithm, and implementation of a co-managing scheme called Duet, which automatically trades off perceptual quality for energy efficiency in accordance with static and dynamic visual acuity when users interact with mobile applications. The results of experiments conducted on a commercial smartphone with popular interactive apps show that Duet saves more energy while retaining better visual quality, compared with a joint scheme that simultaneously uses dynamic pixel disabling and dynamic resolution scaling to save OLED and GPU energy in isolation.
In emergency medical services, the lag time between injury and treatment is one of the most critical parameters with respect to patient survivability. Ambulance services aim to maximize the likelihood of prompt medical treatment to prevent death and/or potential non-reversible damages. The emerging Tactile Internet has a vital role to play on that frontier by allowing next generation of ambulances to be equipped with advanced haptic/tactile devices to allow pre-hospital treatment/diagnosis or even remote surgery while en route. In this paper we propose a novel reliable multi-modal e-health high mobility service optimization framework for ambulances utilizing mobile edge clouds to efficiently transport real time patient information to the hospital. The main challenge of the proposed e-health service is to guarantee the heterogeneous QoS requirements of all involved data flows between the ambulance and the medical personnel. To this end, we formulate the service configuration problem as an optimization problem. In addition, a set of low-complexity algorithms are proposed to provide competitive solutions in real-time. A comprehensive set of numerical investigations are presented to characterize the attainable system performance of the proposed schemes.
Deal selection on Groupon is a typical social learning and decision making process, where the quality of a deal is usually unknown to the customers. The customers must acquire this knowledge through social learning from other social medias such as reviews on Yelp. Additionally, the quality of a deal depends on both the state of the vendor and decisions of other customers on Groupon. How social learning and network externality affect the decisions of customers in deal selection on Groupon is our main interest. We develop a data-driven game-theoretic framework to understand the rational deal selection behaviors cross social medias. The sufficient condition of the Nash equilibrium is identified. A value-iteration algorithm is proposed to find the optimal deal selection strategy. We conduct a year-long experiment to trace the competitions among deals on Groupon and the corresponding Yelp ratings. We utilize the dataset to analyze the deal selection game with realistic settings. Finally, the performance of the proposed social learning framework is evaluated with real data. The results suggest that customers do make decisions in a rational way instead of following naive strategies, and there is still room to improve their decisions with assistance from the proposed framework.
In this paper, we consider a simultaneous wireless information and power transfer (SWIPT)-enabled cooperative cognitive network that addresses energy scarcity and spectral scarcity, two important issues in 5G wireless communications. In the considered network, the self-sustainable, SWIPT-enabled relay assists primary user's transmission, while the relay itself is also a secondary user with its own information superimposed on the regenerated primary information for transmission. The SWIPT relay employs the proposed energy-assisted decode-and-forward (EDF) protocol, which enhances the conventional decode-and-forward (DF) protocol with energy-dimension-augmented information decoding. We conduct a comparative analysis of the proposed EDF and the conventional DF and amplify-and-forward (AF) protocols in this SWIPT cooperative cognitive framework in terms of capacity, outage probability, and throughput for both primary and secondary networks. Simulation corroborates the analysis and demonstrates performance advantages of EDF over DF/AF from various perspectives.
In this paper, we propose a smartphone-based hearing assistive system (termed SmartHear) to facilitate speech recognition for various target users who could benefit from enhanced listening clarity in the classroom. The SmartHear system consists of transmitter and receiver devices (e.g., smartphone and Bluetooth headset) for voice transmission, and an Android mobile application that controls and connects the different devices via Bluetooth or WiFi technology. The wireless transmission of voice signals between devices overcomes the reverberation and ambient noise effects in the classroom. The main functionalities of SmartHear include: 1) configurable transmitter/receiver assignment, to allow flexible designation of transmitter/receiver roles; 2) advanced noise-reduction techniques; 3) audio recording; and 4) voice-to-text conversion, to give students visual text aid. All the functions are implemented as a mobile application with an easy-to-navigate user interface. Experiments show the effectiveness of the noise-reduction schemes at low signal-to-noise ratios (SNR) in terms of standard speech perception and quality indices, and show the effectiveness of SmartHear in maintaining voice-to-text conversion accuracy regardless of the distance between the speaker and listener. Future applications of SmartHear are also discussed.
Agents in networks often encounter circumstances requiring them to make decisions. Nevertheless, the effectiveness of the decisions may be uncertain due to the unknown system state and the uncontrollable externality. The uncertainty can be eliminated through learning from information sources, such as user-generated contents or revealed actions. Nevertheless, the user-generated contents could be untrustworthy since other agents may maliciously create misleading contents for their selfish interests. The passively revealed actions are potentially more trustworthy and also easier to be gathered through simple observations. In this paper, we propose a new stochastic game-theoretic framework, Hidden Chinese Restaurant Game (H-CRG), to utilize the passively revealed actions in stochastic social learning process. We propose grand information extraction, a novel Bayesian belief extraction process, to extract the belief on the hidden information directly from the observed actions. We utilize the coupling relation between belief and policy to transform the original continuous belief-state Markov decision process (MDP) into a discrete-state MDP. The optimal policy is then analyzed in both centralized and game-theoretic approaches. We demonstrate how the proposed H-CRG can be applied to the channel access problem in cognitive radio networks. We then conduct data-driven simulations using the CRAWDAD Dartmouth campus wireless local area network (WLAN) trace. The simulation results show that the equilibrium strategy derived in H-CRG provides higher expected utilities for new users and maintains a reasonable high social welfare comparing with other candidate strategies.
OLED power management on mobile devices is very challenging due to the dynamic nature of human-screen interaction. This paper presents the design, algorithms, and implementation of a lightweight mobile app called ShiftMask, which allows the user to dynamically shift OLED power to the portion of interest, while dimming the remainder of the screen based on visual acuity. To adapt to the user’s focus of attention, we propose efficient algorithms that consider visual fixation in static scenes, as well as changes in focus and screen scrolling. The results of experiments conducted on a commercial smartphone with popular interactive apps demonstrate that ShiftMask can achieve substantial energy savings, while preserving acceptable readability.
This paper considers a device-to-device (D2D) communications underlaid multiple-input multiple-output (MIMO) cellular network and studies D2D mode selection from a previously unexamined perspective. Since D2D mode selection affects the network interference profile and vice versa, a joint D2D mode selection and interference management is desired but challenging. In this work, we propose a holistic approach to this problem with interference-free considerations. We adopt the degrees-of-freedom (DoF) as the mode-selection criterion and exploit the linear interference alignment (IA) technique for interference management. We analyze the achievable sum DoF of the potential D2D users according to their mode selections, and derive the probabilistic sum-rate relations between the proposed DoF-based mode selection scheme and the common received-signal-strength-index (RSSI)-based mode selection scheme in Poisson point process (PPP) networks. Simulation illustrates the theoretical insights and shows the advantages of the proposed DoF-based mode selection scheme over conventional mode selection schemes from various perspectives. The proposed scheme presents a promising proposal for D2D mode selection in 5G communications.