Research shows that the head posture not only contains important interpersonal information but also is an external manifestation of human psychological activities. Head posture plays an important role in automotive safety, smart home, and other intelligent environments. Rf-based posture recognition method provides a non-contact and privacy protection method to detect and monitor human activities. However, how to separate weak activity state information from reflected signals has been a big challenge for this kind of method. This paper proposes HeadSee, a passive human head gesture sensing system built on a cheap commodity RFID device. Without attaching any device to the human body, HeadSee using ICA extracts the weak reflected RF signals from the human body for gesture sensing. And then HeadSee carefully models the head movement by utilizing the signal's phase/RSS (received signal strength) changes and successfully quantifies the head gesture with continuous sequences of movement states. Extensive experiments show that even with interfering movements from other body parts, HeadSee can still achieve around 91% recognition accuracy of the head gestures.

Wireless sensor networks as the base support for the Internet of things have been a large number of popularity and application. Such as intelligent agriculture, we have to use the sensor network to obtain the growing environment data of crops and others. However, the difficulty of power supply of wireless nodes has seriously hindered the application and development of Internet of things. In order to solve this problem, people use low-power sleep scheduling and other energy-saving methods on the nodes. Although these methods can prolong the working time of nodes, they will eventually become invalid because of the exhaustion of energy. The use of solar energy, wind energy, and wireless signals in the environment to obtain energy is another way to solve the energy problem of nodes. However, these methods are affected by weather, environment, and other factors, and they are unstable. Thus, the discontinuity work of the node is caused. In recent years, the development of wireless power transfer (WPT) has brought another solution to this problem. In this paper, a three-layer framework is proposed for mobile station data collection in rechargeable wireless sensor networks to keep the node running forever, named TLFW which includes the sensor layer, cluster head layer, and mobile station layer. And the framework can minimize the total energy consumption of the system. The simulation results show that the scheme can reduce the energy consumption of the entire system, compared with a Mobile Station in a Rechargeable Sensor Network (MSiRSN).

Elderly people generally suffer from diseases with cognitive decline, which seriously affect their daily lives. To assist these patients with cognitive decline, numerous cognitive aid devices have been researched and designed. Although these devices can achieve the aid tasks, they have to face two difficulties: low accuracy and long latency. In this paper, we present MobileAid, an aid system implemented on the mobile device, which assists cognitive decline patients to recognize objects. The key idea of this system is a two-step lightweight neural network design for the target recognition: context recognition and object recognition, and this design achieves low time delay. Considering the great success of convolutional neural networks in object recognition, we design a lightweight convolutional neural network by the combination of pooling layers and activation functions selection, which achieves high accuracy. Furthermore, we apply the depthwise separable convolution to reduce the resource consumption for deploying the system on mobile devices. The results of the extensive experiments we have conducted show that MobileAid can achieve 95% high accuracy and 90ms low time delay with few resource consumption.

Mobile crowd sensing is a novel large-scale sensing pattern, which applies users’ smart devices to analyze social context and human activity, and then learns the intelligent information serving for various innovative services. An augmented reality (AR) system can bring more authentic experience for human life, and more and more scholars are increasingly interested in this technology. However, there are some problems in the previous methods, such as the expensive deployment, low accuracy, and high latency. These problems greatly limit the application of augmented reality systems. In this paper, we design a lightweight augmented reality system, called LAR, which can recognize the target object quickly and precisely by using a feature matching algorithm. In LAR, the target object is shot twice to get the distance between the user and the object, and we take the distance as the feature of the target object. Furthermore, the feature extraction algorithm, named s-SURF, is designed to extract image features. LAR combines the distance with image feature and then matches with these images stored in database. In addition, the flutter-free algorithm is used to denoise and get a clearer image. Finally, we design a prototype system to evaluate LAR performance. The accuracy of LAR is 86 % , and the time delay achieves 141 ms.

An increasing number of applications in scientific and other domains have moved or are in active transition to clouds, and the demand for big data transfers between geographically distributed cloud-based data centers is rapidly growing. Many modern backbone networks leverage logically centralized controllers based on software-defined networking (SDN) to provide advance bandwidth reservation for data transfer requests. How to fully utilize the bandwidth resources of the links connecting data centers with guaranteed quality of service for each user request is an important problem for cloud service providers. Most existing work focuses on bandwidth scheduling for a single request for data transfer or multiple requests using the same service model. In this work, we construct rigorous cost models to quantify user satisfaction degree, and formulate a generic problem of bandwidth scheduling for multiple deadline-constrained data transfer requests of different types to maximize the request scheduling success ratio while minimizing the data transfer completion time of each request. We prove this problem to be not only NP-complete but also non-approximable, and hence design a heuristic algorithm. For performance evaluation, we establish a proof-of-concept emulated SDN testbed and also generate large-scale simulation networks. Both experimental and simulation results show that the proposed scheduling scheme significantly outperforms existing methods in terms of user satisfaction degree and scheduling success ratio.

Handheld mobile photography is often affected by motion blur due to the difficulty of keeping the camera's stable. The existing processing method is usually a high-cost deblurring process of a computer, which seriously affects the user experience, and the deblurring effect is poor due to the lack of information on camera motion. Inspire by edging computing's ongoing efforts in automatically and collaboratively process more types of resources in the edge and cloud. In this paper, we present SID, a sensor-assisted image deblurring system for mobile devices. Using information about camera motion acquired from built-in sensors from smartphones (e.g. accelerometers, gyroscopes, and magnetometers), then estimating the point spread function, and combining the image's segmentation smoothing characteristics with spatial adaptation to image Deblurring. The image is preprocessed by the p-m nonlinear diffusion model, which preserves the characteristics of the image. The fuzzy image confirmation avoids the damage to the original high quality image. Wiener-Hoff optimization is used to optimize the effect of image restoration. We evaluated 400 photos with varying degrees of blur and size. Compared to traditional blind and unblind deconvolution methods, our algorithm shows significant advantages in both deblurring and processing delays.

LoRa, one of the most potential LPWAN (Low-Power Wide Area Network) techniques, has received widespread attention for its far transmission distance and long battery life. These characteristics make it successfully applied in target tracking, water level monitoring, fire alarm, smart city, etc. Since these applications mainly require tens of hundreds of access devices to collect data, different LoRa networks will overlap, so an interference-free network is badly needed. Current collision avoidance scheme, however, could cause collisions again, especially when the the duty cycle of interference source is high. In other words, current scheme is not interference-free. Motivated by the exist interference, this paper presents an interference-aware concurrent transmission model: LoRaSense. Specifically, LoRaSense estimates the idle cycles of interference source through interference-aware model based on RSSI, and then achieves concurrent transmission of access devices and interference sources combining with collision model. Our LoRaSense increases channel utilization while resisting interference. To demonstrate the utility of LoRaSense, we build a prototype of LoRaSense in one LoRa gateway and three LoRa nodes. Our real-world experiments show that LoRaSense can achieve 10%-15% packet reception ratio improvement compared to LoRaWAN.

The mobile devices photographing has greatly enriched people’s interest, social and entertainment. The improvement of mobile devices processing chip and storage make the quality and quantity of pictures in mobile increased rapidly. Although mobile images can be simply categorized in existing work, there is no recommendation[5] list based on user’s mind. This situation will bring great burden and poor experience to users when selecting image. In this paper, we proposed an plug-in system, named IRI(IRI, Image Recommendation with User Intent), to create a recommendation list which follows user’s mind. In IRI, the user intent[1] can be sensed employing the text input. The multi-layer semantic relation library is used for assessing correlation between the image and sensed intent. We implement the IRI on mobile phone and test the accuracy of the intent sensing and energy consumption. The experimental results demonstrate the effectiveness and superiority of the IRI.

Cooperative vehicle infrastructure system (CVIS) is an essential part of the future intelligent transportation system (ITS). However, CVIS is uneasy to meet different QoS requirements of various information flows in the existing traffic environment. In fact, some real-time applications highly related to traffic safety early warning cannot be easily achieved or passengers' Internet accessing experience is always not very good. To overcome the above problem, this paper proposes a communication protocol named data networking for vehicle infrastructure cooperation (NDNVIC), which can respond to the vehicle's data query timely. In this paper, we utilize some ZigBee nodes as vehicle and RSU nodes and conduct several simulation experiments under a large-scale deployment with 150 RSU nodes to verify the effectiveness of NDNVIC. Compared with the traditional TCP/IP protocol, the response rate of NDNVIC is about four times, as fast as, that of TCP/IP.

Gestures recognition as the main technology of human-computer interaction draws a great amount attention of researchers. Comparing to existing methods, the RFID-based passive gesture recognition requires no specialized equipment which makes it much easier to be used. To achieve the goal, we build a priori gesture database according to signal features caused by perturbation of different gestures. Then, the modified dynamic time warping (DTW) algorithm has been used to match with the priori fingerprint database. Besides, we propose a wireless phase calibration algorithm by utilizing the theory that the noise subspace and the signal subspace is orthogonal in multiple signal classification (MUSIC) algorithm to estimate and remove phase errors that may caused by equipment differences so that we can ensure the accuracy of angle of arrival (AoA) estimation. To evaluate the effectiveness of our gesture recognition system, the experiments in a real scene were carried out. And the experimental results show that we can achieve about 92% accuracy.

The popularity of campus information intelligence aims to provide better applications and services. However, we found that a phenomenon that has brought great inconvenience to students is queuing when pay in smart campus which needs to be resolved urgently. For example, buying meals in the school cafeteria at lunch or dinner time, paying school tuition, and so on. Also the privacy security is the primary prerequisite for smart campus's mobile electronic payment while transaction information can be intercepted easily by Hacker or hidden HD camera. In this paper, we propose the SafeMP, a mobile security and multi-to-one payment system employing the mechanical-wave transmission to reduce queue time and improve efficiency. The biggest conundrum with implementing this system is how to separate the multiple aliasing signals. The SafeMP is designed with a key finding: although the multiple aliasing signals are irregular, coupling degree between different mechanical wave signals is highly stable. We divide our work into three parts. First, we present a novel compensation method addressing the attenuation with the transmission distance. In addition, we define the degree of coupling to discriminate the aliasing for mechanical wave in ever slot. Finally, we design the rules of orthogonal coding to further connect the data bitstream of each time slot and restore the complete information which transmitted by different user, then the information separated can be corresponded to correct user. Our evaluation results show that the transmission distance does not influence the performance of the SafeMP. The SafeMP achieves an average accuracy of 97%.

Target material identification is playing an important role in our everyday life. This paper introduces a device-free target material identification system, implemented on ubiquitous and cheap commercial off-the-shelf (COTS) Wi-Fi devices. The intuition is that different materials produce different amounts of phase and amplitude changes when a target appears on the line-of-sight (LoS) of a radio frequency (RF) link. However, due to multipath and hardware imperfection, the measured phase and amplitude of the channel state information (CSI) are very noisy. We thus present novel CSI pre-processing schemes to address the multipath and hardware noise issues before they can be used for accurate material sensing. Comprehensive real-life experiments demonstrate that we can identify 10 commonly seen liquids at an overall accuracy higher than 95% with strong multipath indoors.

In wireless sensor networks (WSNs), Radio Signal Strength Indicator (RSSI)-based localization techniques have been widely used in various applications, such as intrusion detection, battlefield surveillance, and animal monitoring. One fundamental performance measure in those applications is the sensing coverage of WSNs. Insufficient coverage will significantly reduce the effectiveness of the applications. However, most existing studies on coverage assume that the sensing range of a sensor node is a disk, and the disk coverage model is too simplistic for many localization techniques. Moreover, there are some localization techniques of WSNs whose coverage model is non-disk, such as RSSI-based localization techniques. In this paper, we focus on detecting and recovering coverage holes of WSNs to enhance RSSI-based localization techniques whose coverage model is an ellipse. We propose an algorithm inspired by Voronoi tessellation and Delaunay triangulation to detect and recover coverage holes. Simulation results show that our algorithm can recover all holes and can reach any set coverage rate, up to 100% coverage.

This paper presents DeepAid, a mobile system assisting cognitive decline patients. Existing designs of such systems suffer low accuracies for the recognition and long latencies for the interaction two major issues. DeepAid leverages deep learning to solve both issues at the same time. Deep learning naturally leads to high-accuracy object recognition. If it can further execute on the local device directly, the latency can be satisfied as well since frequent data transmission to the server in prior approaches can be avoided. The major contribution of this paper is to instrument the possibility that deep learning can be compact so as to achieve a good trade-off between accuracy and resource consumption, with the design of task decomposition and network scale minimization. To validate the effectiveness of DeepAid, we implement a series of experiments and compare with other related work. In the final, the DeepAid can achieve about 97% accuracy in object recognition with about 90ms time delay.

Without requiring targets to carry any device, device-free-based tracking is playing an important role in many emerging applications such as smart homes, fitness tracking, intruder detection, etc. While promising, current device-free tracking systems based on inexpensive commercial devices perform well in the training environment, but poorly in other environments because of different multipath reflections. This paper introduces RDTrack, a system that leverages changes in Doppler shifts, which are not sensitive to multipath, to accurately track the target. Moreover, RDTrack identifies particular patterns for fine-grained motions such as turning, walking straightly, etc., which can achieve accurate tracking. For the purpose of achieving a fine-grained device-free tracking system, this paper builds a trajectory estimating model using HMM (Hidden Markov Model) to improve the matching accuracy and reduce the time complexity. We address several challenges including estimating the tag influenced time period, identifying moving path and reducing false positives due to multipath. We implement RDTrack with inexpensive commercial off-the-shelf RFID (Radio Frequency IDentification) hardware and extensively evaluate RDTrack in a lobby, staircase and library. Our results show that RDTrack is effective in tracking the moving target, with a low tracking error of 32 cm. This accuracy is robust for different environments, highlighting RDTrack's ability to enable future essential device-free moving-based interaction with RFID devices.

Performance of a collaborative task is mostly dependent on the collective effort from participants. To accomplish a participatory task effectively and efficiently, the team formation problem (TFP) outweighs all other considerations. It is even more complicated when social connections among candidates is taken into account. As we can imagine, a large number of tasks require members of the team to be socially close. On the contrary, a portion of tasks, e.g., proposal review, pay more attention to a multidimensional view, and team members should be selected from a variety of cliques. Due to the nature of tasks, it is challenging to find a subset that meets the skill requirement of the task as well as socially diversity demand of team members from a pool of candidates. In this paper, we explore the TFP in a social network. Based on different task objectives, we first formulate the TFP as TFP with strong ties (TFP-ST) and TFP with weak ties (TFP-WT), respectively. Both TFP-ST and TFP-WT are proven to be NP-hard, and we then design corresponding heuristic algorithms to solve the two problems. Through extensive simulations, we show that the solution to TFP-ST can achieve significant improvement in terms of collaboration cost, team size, as well as running time, and the solution to TFP-WT can provide better performance than existing approaches at the same time.

This paper investigates the robust and reliable transmission problem in Internet of Things (IoT) applications, where multiple gateways are deployed. We discover that the reliable routing path with the best link qualities may not always gain the reliable transmission. The main reason is that the majority of existing routing metrics generally do not consider the working state of gateways. And the gateways may significantly reduce the reliability of data transmission at the last hop when they operate at the variable duty cycles (e.g., due to insufficient energy harvesting from ambiance). Last-hop data loss will lead to the inefficient transmission in all previous hops. To address this issue, we propose a novel routing metric ETD (Expected Transmission Direction, ETD), which efficiently selects a proper set of gateways with improved reliability in variable duty-cycled IoT through estimating the working state of gateways. Based on ETD, we design an efficient opportunistic routing protocol PoR to ensure reliable data transmission. Our simulations demonstrate the superior performance of PoR. It is shown that PoR achieves over 98% packet delivery ratio even in the worst network setting, with effective load balancing among selected gateways.

Wireless Sensor Networks (WSNs) based on RSSI-based localization techniques have been widely used in various applications, such as intrusion detection, battlefield surveillance, and animal monitoring. One fundamental performance measure in those applications is the sensing coverage of the target field. Most existing works on coverage assume that the sensing area of a sensor node is a disc. However, this disc model is too simplistic for many information processing systems, and there are sensing techniques whose sensing areas are non-disc. In this paper, we focus on coverage performance of WSNs based on RSSI-based localization techniques whose sensing area is an ellipse. We propose an algorithm inspired by Voronoi diagram and Delaunay triangulation to detect and recover coverage holes. Simulation results show that our algorithm can recover all holes, and can reach any set coverage rate, up to 100% coverage, while using the minimum number of sensors.

As the underlying infrastructure of the Internet of Things (IoT), wireless sensor networks (WSNs) have been widely used in many applications. Network coding is a technique in WSNs to combine multiple channels of data in one transmission, wherever possible, to save node's energy as well as increase the network throughput. So far most works on network coding are based on two assumptions to determine coding opportunities: (1) All the links in the network have the same transmission success rate; (2) Each link is bidirectional, and has the same transmission success rate on both ways. However, these assumptions may not be true in many actual WSNsthe wireless links among nodes are often subject to all kinds of disturbance, obstruction, etc., and may transmit with different success rates. This paper proposes a new routing strategy, named Adaptive Network Coding Routing (ANCR). ANCR firstly establishes a routing path with the traditional network coding routing (NCR), and then applies the neighborhood search algorithm to adaptively determine nodes' coding opportunities based on the links' transmission success rates, with the target of reducing the total number of transmission. The simulation results show that, in WSNs with different-success-rate links, ANCR can reduce the network delay by about 50%, and increase the network throughput by about 67%, compared with the traditional NCR.

Abstract:To address the problem of data flow leak caused by taint analysis,a method for binary code anti-Taint analysis was proposed.The core code was located by implanting tags during development or typing the address after development.To protect the sensitive data and improve the ability of resisting taint analysis,the anti-taint analysis was designed by bleaching the taint.Besides,for the variety of protective methods,three kinds of anti-taint analysis methods were proposed respectively for data substitution,data match and stack operation.The results showed that the proposed methods can effectively improve the ability of resisting taint analysis and would not result a large performance loss.