Hansong Guo

Spectrum combinatorial double auction for cognitive radio network with ubiquitous network resource providers

Spectrum auction is an emerging economic scheme to stimulate both primary spectrum operators (POs) and secondary users (SUs) to be involved in spectrum sharing. Previous spectrum auction works mostly assume each PO can only have one type spectrum or each SU can only buy homogeneous spectrum bands from the same PO. However, in a ubiquitous network scenario, each PO possesses heterogeneous spectrum resources such as WiFi, 3G and each SU may request different types of spectrum bands from the same PO. Existing auction schemes cannot be used to effectively solve the problem. Therefore, the authors come out with a lightweight combinatorial double auction to tackle this challenge. Since spectrum combinatorial double auction problem is NP-hard, the authors develop a general greedy algorithm G-Greedy to solve the problem. Inspired by the recent group-buying discounts, they also invent an enhanced scheme E-Greedy to further optimise total utility. They theoretically prove the economy properties of the proposed schemes such as individual rationality, budget balance and truthfulness. Simulation results show that both of the two algorithms can yield higher utilities and are effective.

iProtect: Detecting Physical Assault Using Smartphone

Motivated by the reports about assaults on women, especially college girls, in China, we take the first step to explore possibility of using off-the-shelf smartphone for physical assault detection. The most difficult one among challenges in our design is the extraordinary complexity and diversity of various assault instances, which lead to an extremely hard, if not impossible, to perform fine-grained recognition. To this end, we decide to focus on the characteristics of intensity and irregularity, based on which several features are extracted. Moreover, we proposed a combinatorial classification scheme considering individuality of user’s ADLs(Activities of Daily Living) and universality of differences between ADLs and assaults to most people. The data we used for evaluation are collected from simulated assaults which are performed by our volunteers in controlled settings. Our experiment results showed that physical assaults could be distinguished with the majority of ADLs in our proposed feature space, and our proposed system could correctly detect most instances of aggravated assault with low false alarm rate and short delay.

SOS: Real-time and accurate physical assault detection using smartphone

Motivated by the reports about assaults on women, we take the first step to explore possibility of using off-the-shelf smartphone for physical assault detection. There are several kinds of crime offenses against persons, such as gunshot, battery, abuse, kidnapping and so on, which are distinguished by form, severity, duration, etc. In this paper, we aim at detecting those severe and non-instantaneous physical assaults using accelerometer in smartphone. We collected 100 surveillance videos involving aggravated assaults, and extract the the pattern of actions for an individual under assaulting. The most difficult one among challenges in our design is the extraordinary complexity and diversity of actions under assaulting, which lead to an extremely hard, if not impossible, to perform fine-grained recognition. To this end, we decide to focus on the intensity and irregularity characteristics of aggravated assaults, based on which several features from time domain and frequency domain are extracted. Moreover, we proposed a combinatorial classification scheme considering individuality of user’s ADLs (Activities of Daily Living) and universality of differences between ADLs and assaults to most people. The data we used for training and testing are collected from simulated aggravated assaults which are performed by our volunteers in controlled settings. Our experiment results showed that aggravated assaults could be distinguished with the majority of ADLs in our proposed feature space, and our proposed system could correctly detect most instances of aggravated assault (FNR = 11.75 %) with low false alarm rate (0.067 times per day) and short delay (6.89 s).

Recognizing the Operating Hand and the Hand-Changing Process for User Interface Adjustment on Smartphones

As the size of smartphone touchscreens has become larger and larger in recent years, operability with a single hand is getting worse, especially for female users. We envision that user experience can be significantly improved if smartphones are able to recognize the current operating hand, detect the hand-changing process and then adjust the user interfaces subsequently. In this paper, we proposed, implemented and evaluated two novel systems. The first one leverages the user-generated touchscreen traces to recognize the current operating hand, and the second one utilizes the accelerometer and gyroscope data of all kinds of activities in the user’s daily life to detect the hand-changing process. These two systems are based on two supervised classifiers constructed from a series of refined touchscreen trace, accelerometer and gyroscope features. As opposed to existing solutions that all require users to select the current operating hand or confirm the hand-changing process manually, our systems follow much more convenient and practical methods and allow users to change the operating hand frequently without any harm to the user experience. We conduct extensive experiments on Samsung Galaxy S4 smartphones, and the evaluation results demonstrate that our proposed systems can recognize the current operating hand and detect the hand-changing process with 94.1% and 93.9% precision and 94.1% and 93.7% True Positive Rates (TPR) respectively, when deciding with a single touchscreen trace or accelerometer-gyroscope data segment, and the False Positive Rates (FPR) are as low as 2.6% and 0.7% accordingly. These two systems can either work completely independently and achieve pretty high accuracies or work jointly to further improve the recognition accuracy.

Recognizing the Operating Hand from Touchscreen Traces on Smartphones

As the size of smartphone touchscreens becomes larger and larger in recent years, operability with single hand is getting worse especially for female users. We envision that user experience can be significantly improved if smartphones are able to detect the current operating hand and adjust the UI subsequently. In this paper, we propose a novel scheme that leverages user-generated touchscreen traces to recognize current operating hand accurately, with the help of a supervised classifier constructed from twelve different kinds of touchscreen trace features. As opposed to existing solutions that all require users to select the current operating hand or dominant hand manually, our scheme follows a more convenient and practical manner, and allows users to change operating hand frequently without any harm to user experience. We conduct a series of real-world experiments on Samsung Galaxy S4 smartphones, and evaluation results demonstrate that our proposed approach achieves 94.1% accuracy when deciding with a single trace only, and the false positive rate is as low as 2.6%.

STRUCTURE: A Strategyproof Double Auction for Heterogeneous Secondary Spectrum Markets

Auction has been regarded as one of the promising methods for the scarce resources allocation due to its fairness. Thus, spectrum auction is an efficient way to allocate licensed spectrum to new demanders for mitigating the spectrum scarcity. Most of the existing studies assume that the spectrum resources are homogeneous. However, spectrums with different frequencies are intrinsically heterogeneous due to their different licensed areas and interference ranges. In this paper, we concentrate on the heterogeneity of spectrum resources and propose a strategyproof double auction mechanism STRUCTURE. The STRUCTURE assumes that all the buyers are selfish and rational, and they will submit their bids for each interested spectrum. To achieve the strategyproofness, many existing double spectrum auction mechanisms adopt the bid-independent methods to construct buyer groups, which may cause unfairness for the buyers with high bid values. To tackle this, we turn to choose a bid-related buyer group construction algorithm, which is more suitable for the laws of market and can further avoid the collusion between buyers. After that, we propose a collusion-free allocation mechanism and a bid-independent payment mechanism to ensure the strategyproofness for both buyers and sellers. Simulation results show that the proposed mechanism significantly improves the spectrum utilization with low running time. Furthermore, we also find that the buyers with higher bid values have a higher winning ratio than the buyers with low bids in the STRUCTURE.

Optimal channel allocation for multi-PU and multi-SU pairs in underlay cognitive radio networks

In the underlay cognitive radio networks, this paper defines the joint channel and power allocation problem, which aims to optimise the max-total and max-min throughputs of secondary users (SUs), with the constraints of interference on primary receivers. For the max-total problem, we formulate the problem as a bipartite matching and derive a maximum weighted matching-based sum throughput maximisation algorithm (STMA) to solve this problem. For the max-min problem, on the basis of the optimal relay assignment (ORA) algorithm, we derive a polynomial time optimal channel assignment algorithm (OCAA) to iteratively assign channels to each SU pair under the power constraint. Simulation results demonstrate the effectiveness of our algorithms when compared with random method.

iRun: A Smartphone-Based System to Alert Runners to Warm Up Before Running

Running is a good way to keep healthy and relax, while many runners suffer from injuries because of a lack of running knowledge and ignoring the importance of warm-up. Inspired by the fact that more and more people run with smartphones tied up to their arms, we propose a novel system named iRun to alert people to warm up before running. iRun is based on the sensors built in most off-the-shelf smartphones like accelerometers, and it uses human activity recognition (HAR) methods to detect whether the runners warm up or not. The most challenging work is to choose the features that can represent the characteristic of various warm-up actions because different people have different exercise habits. By carefully designing the feature vector which contains features from multi-domains and doing a series of experiments to decide the slide window size and classifier, iRun can achieve 91.4 % true positive (TP) rate in average to distinguish every warm-up action from other movements like running, walk, going upstairs, etc.

Tefnut: An Accurate Smartphone Based Rain Detection System in Vehicles

Real-time and fine-grained rain information is crucial not only for climate research, weather prediction, water resources management, agricultural production, urban planning and natural disasters monitoring, but also for applications in our daily lives. However, because of the lack of rain detection systems and the high variable attribute of rain, both in time and space, the rain detection today is still not precise enough. In such context, we propose and implement Tefnut (Tefnut is the rain deity in Ancient Egyptian religion.), a novel system that exploits opportunistically crowdsourced in-vehicle audio clips from an alternative, nowadays omnipresent source, smartphones, to achieve precise detection of rain leveraging a supervised recognizer constructed from a series of refined features. We conduct extensive experiments, and evaluation results demonstrate that Tefnut can detect the rain with 96.0 % true positive rate, when deciding with a one-second-long in-vehicle audio segment only.

i-Shield: A System to Protect the Security of Your Smartphone

Losing smartphones is a troublesome thing as smartphones are playing an important role in our daily lives. As smartwatches become popular, we argue that smartwatches can play a role in smartphone antitheft design. In this paper, we propose i-Shield, a real-time antitheft system that leverages accelerometers and gyroscopes of smartphones and smartwatches to prevent smartphone being stolen. As opposed to existing solutions which are based on Bluetooth, NFC, or GPS tracking, i-Shield follows a practical manner to achieve the goal of real-time antitheft for smartphones. i-Shield recognizes taken-out events of smartphones using a supervised classifier, and applies a dynamic time warping (DTW) scheme to recognize whether the events are caused by users themselves. We conduct a series of experiments on iPhone6 and iPhone4s, and the evaluation results show that our system can achieve 97.4 % true positive rate of recognizing taken-out actions, and classify taken-out actions with misclassification rate of 1.12 %.