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In dairy herd management, it is significant and irreplaceable for veterinarians to make rapid and effective diagnosis of dairy cow diseases. Based on electronic medical records, deep learning (DL) has been widely used to support clinical decisions for humans. However, this method is rarely adopted in veterinary diagnosis. In addition, most DL models are driven by large datasets, failing to utilize the knowledge acquired by veterinarians in subjective experience, which is critical to disease diagnosis. To address these problems, this paper proposes a DL method for disease diagnosis of dairy cow: convolutional neural network (CNN) based on knowledge graph and transfer learning (KGTL_CNN). Firstly, the structural knowledge was extracted from a knowledge graph of dairy cow diseases, and treated as part of the inputs to the CNN based on knowledge graph (KG_CNN). Then, the model performance was enhanced through pre-training by transfer learning. To verify its performance, experiments were carried out on dairy cow clinical datasets. The results show that our model performed satisfactorily on disease diagnosis: the KG_CNN and KGTL_CNN achieved an F1-score of 85.87% and 86.77%, respectively, higher than that of typical CNN by 6.58% and 7.7%. The research results greatly promote the effective, fast, and automatic clinical diagnosis of dairy cow diseases.

A notch continuum manipulator with a large cavity is presented. The continuum manipulator which is designed for laryngeal surgery is composed of guidewire discs and a Nitinol skeleton. The deformation of the continuum manipulator is superimposed by the flexible units, therefore its posture and driving force are coupled. Based on the small deformation cantilever beam theory, the mechanical model from the microscopic to the macroscopic is used to establish the mapping from the driving space to the joint space. The manipulator is assumed to be a multi-joint series robot. Based on the mechanical model, the mapping from the joint space to the Cartesian space is established by the Denavit-Hartenberg (D-H) method. The kinematics is established from the driving space to the Cartesian space. The proposed kinematic model is verified by a prototype. The experimental results show the validity of the model.

In order to explore the interaction mechanism between brain regions of Alzheimer's disease (AD), brain connectivity was constructed and analyzed by using background electroencephalogram (EEG) signals recorded from AD patients and the normal controls. In this work, a linear coherent network was constructed, and AD brain network showed the disconnected characteristics. Then eight network measures of each frequency band (delta, theta, alpha and beta) were extracted, which used as the input features for the support vector machine (SVM) classifier. In order to study the correlation between features under different frequency bands, the following three feature input schemes of SVM were applied, that are, (1) single feature of within-frequency band; (2) multi-features of within-frequency band and (3) multi-features of cross-frequency band. The experimental results showed that the highest accuracy of the scheme (1) was 0.8225, while the accuracy of the scheme (2) was 0.91, which was higher than that of the scheme (1). The final accuracy of the scheme (3) was 0.9545. The classification results showed that the combined features in the cross-frequency band could classify the AD group and the control group with high accuracy, which represents that frequency coupling in nervous system is associated with impaired and normal brain functions. These findings suggest that the feature extracted from the coherence network and the method of feature combination can be effectively distinguished AD and the control group, which can give us a deeper understanding of AD brains.

In this paper, a complete mapping, localization and navigation framework based on ROS system is proposed. And on this experimental platform, a variety of environments are built based on the 3D simulation software gazebo. In the simulation experiment, gmapping and cartographer are used to build occupancy map in real time. The evaluation strategy of the SLAM system is proposed, and the experiment result evaluation based on different environment and parameters is completed.

Firewalls are crucial elements that enhance network security by examining the field values of every packet and deciding whether to accept or discard a packet according to the firewall policies. With the development of networks, the number of rules in firewalls has rapidly increased, consequently degrading network performance. In addition, because most real-life firewalls have been plagued with policy conflicts, malicious traffics can be allowed or legitimate traffics can be blocked. Moreover, because of the complexity of the firewall policies, it is very important to reduce the number of rules in a firewall while keeping the rule semantics unchanged and the target firewall rules conflict-free. In this study, we make three major contributions. First, we present a new approach in which a geometric model, multidimensional rectilinear polygon, is constructed for the firewall rules compression problem. Second, we propose a new scheme, Firewall Policies Compression (FPC), to compress the multidimensional firewall rules based on this geometric model. Third, we conducted extensive experiments to evaluate the performance of the proposed method. The experimental results demonstrate that the FPC method outperforms the existing approaches, in terms of compression ratio and efficiency while maintaining conflict-free firewall rules.

In this paper we propose a cluster-based platoon handoff protocol (CPHP), in which the platoon is divided into clusters to minimize its handoff delay between two adjacent cells. Via the vehicle-to-vehicle (V2V) communications, multiple clusters are constructed within one platoon and only the cluster head (CH) communicates with the base station (BS) via the vehicle-to-infrastructure (V2I) communications. In this way, the number of V2I links can be greatly saved and the signaling overhead is reduced, thereby shortening the handoff delay. Specifically, we formulate a delay minimization problem based on a discrete-time Markov chain and then optimize the number of clusters and spectrum resource allocation. Simulation results demonstrate a significant decrease in the handoff delay of the platoon. The optimal expected delay under a platoon with 50 vehicles by utilizing the proposed CPHP is 15% smaller than that of the traditional handoff scheme.

The explosive growth of the Android malware poses a great threat to users’ privacy and sensitive personal information. It is urgent to develop an effective and efficient Android malware detection system. Existing studies usually require the manual feature engineering for the feature extraction. In fact, the detection performance is heavily relied on the quality of the feature extraction. Additionally, the feature extraction becomes extremely difficult in the malware detection due to the fact that malware developers often deploy the obfuscation techniques. To address this issue, we focus on the Android malware detection using the deep neural networks without the human factors. In this paper, we propose ByteDroid, an Android malware detection scheme that processes the raw Dalvik bytecode using the deep learning. ByteDroid resizes the raw bytecode and constructs a learnable vector representation as the input to the neural network. Then, ByteDroid adopts a Convolutional Neural Networks (CNNs) to automatically extract the malware features and perform the classification. Our experiment results demonstrate that ByteDroid not only can effectively detect Android malware, but also has a great generalization performance given untrained malware. Moreover, ByteDroid maintains resilience to obfuscation techniques.

To better control the individual data flow in the cloud, traffic management policies are in need of increasing fine-grained rules, causing a dramatic increase in rules. The limited CPU or memory resources at the servers become the bottleneck when those policies are employed in large-scale data centers. To overcome these resource constraints, the rule partition algorithm is indispensable to decompose the rule set so that some rules can be migrated away. This paper shows that the current rule partition algorithm of vCRIB may lead to a high traffic overhead and high rule inflation in certain cases. Motivated by this observation, we propose a Fine-grained Rule Partition (FRP) algorithm. Different from vCRIB which divides the rule set based on the unit of rules within the same source address, FRP treats the individual rule as a unit, and thus reduce the traffic overhead by retaining more deny rules. In addition, FRP controls the rule inflation by optimizing the number of redirection rules as well. The simulation results confirm that FRP outperforms vCRIB, especially when the resources are severely constrained.

Optimizing the virtual machine (VM) migration is an important issue of server consolidation in the cloud data center. By leveraging the content similarity among the memory of VMs, the time and the amount of transferred data in VM migration, as well as the pressure of network traffic, can be reduced. There are two problems in server consolidation: (1) determining which VMs should be migrated from the overloaded hosts (VM selection problem) and (2) how to place these VMs to the destination hosts (VM placement problem). By exploiting the content similarity, we redefine the above two problems into one problem to minimize the transferred memory data in VM migration. Given a fixed host overloaded threshold, an approximation algorithm is proposed to solve the problem with one overloaded host and one destination host. For the case of multiple overloaded hosts and destination hosts, two heuristic algorithms are presented with fixed and dynamic overloaded threshold respectively. We conduct a real workload trace based simulation to evaluate the performance of our algorithms. The result shows that our algorithms can produce fewer transferred VM memory data and consume less energy than existing policies.

One of the important sources for big data is the datasets collected by wireless sensor networks. However, errors in sensor data could result in serious damages in industrial applications. Therefore, error detection plays a crucial role in industrial wireless sensor networks (IWSNs). Existing approaches of error detection are generally threshold-based, which rely on a predetermined threshold to judge whether a reading is erroneous. The threshold-based approaches, however, often fail to balance between detection accuracy and false alarm rate. It is thus difficult, if not impossible, to obtain a proper threshold for various errors in real-world applications. Motivated by this consideration, we propose a novel threshold-free error detection approach for IWSNs. By taking the advantage of the spatiotemporal correlations between sensor readings, we present the model to characterize the relationship between sensor pairs and, thus, construct a correlation graph for IWSN. In the correlation graph, the states of nodes, i.e., the states of sensor readings, are accurately exploited without requiring any threshold. Through the experiments on both real sensors and simulations, we demonstrate that the proposed approach is able to significantly improve the detection accuracy while largely reducing the false alarm rate.

Multipath TCP (MPTCP) has been an emerging transport protocol that provides network resilience to failures and improves throughput by splitting a data stream into multiple subflows across all the available multiple paths. While MPTCP is generally beneficial for throughput-sensitive large flows with large number of subflows, it may be harmful for latency-sensitive small flows. MPTCP assigns each subflow a congestion window, making short flows susceptible to timeout when a flow only contains a few packets. This condition becomes even worse when the paths have heterogeneous characteristics as packet reordering occurs and the slow paths can be used with MPTCP, causing the increased end-to-end delay and the lower application Goodput. Thus, it is important to choose the appropriate subflows for each MPTCP connection to achieve the good performance. However, the subflows in MPTCP are determined before a connection is established, and they usually remain unchanged during the lifetime of that connection. To address this issue, we propose DMPTCP, which dynamically adjusts the subflows according to application workloads. Specifically, DMPTCP first utilizes the idea of TCP modeling to estimate the latency on the path under scheduling and the data amount sent on the other paths simultaneously, and then decides the set of subflows to be used for certain application periodically with the goal of reducing completion time for short flows and achieving a higher throughput for long flows. We implement DMPTCP in a Linux server and conduct extensive experiments both in NS3 and in Linux testbed to validate its effectiveness. Our evaluation shows that DMPTCP decreases the completion time by over 46.55% compared to conventional MPTCP for short flows while increases the Goodput up to 21.3% for long-lived flows.

Background: Asthma is a common chronic respiratory disease and is related to air pollution exposure. However, only a few studies have concentrated on the association between air pollution and adult asthma. Moreover, the results of these studies are controversial. Therefore, the present study aimed to analyze the influence of various pollutants on hospitalization due to asthma in adults. Methods: A total of 1019 unrelated hospitalized adult asthma patients from Northeast China were recruited from 2014 to 2016. Daily average concentrations of air pollutants (particulate matter

Although training a deep network with financial time series is not hard, the important issue is, how much the prediction for the truly new data can be trusted with a trained network. In this study, we propose a dynamic predictor selection algorithm (DPSA) that dynamically evaluates and selects the prediction model (predictor) for stock daily movement trend prediction. We first build an initial set of potential candidate predictors based on the convolutional long short-term memory networks (ConvLSTMs) by using different values of parameters. To evaluate the candidate predictors, we propose a kernel time-weighted fuzzy c-means clustering algorithm (KTFCM), which improves the kernel FCM algorithm (KFCM), to organize the historical samples according to their relevance to the target sample, which makes the historical samples that are closely related to the target sample have more influence on the predictors. Then, we use the well-organized historical samples to evaluate the candidate predictors. The predictor that yields the best accuracy is selected to predict the target sample. The proposed DPSA algorithm takes less than one minute in total for training the networks, evaluating and selecting the predictors, and performing prediction, which greatly shortens the time of the deep learning prediction. We perform the comparative experiments for the proposed DPSA algorithm and seven popular methods. These experiments test a large real-life financial time series data of various stock markets. The experiment results show that DPSA achieves the best accuracy and the highest return compared to the seven other popular methods.

Wi-Fi fingerprint based wireless indoor localization has received increasing research attention in recent years. Most existing works utilize the received signal strength (RSS) as the fingerprint of a particular position. However, RSS provides only very coarse-grained property of the received signal and thus cannot achieve high localization accuracy. Recently, some works attempt to improve the localization accuracy of Wi-Fi fingerprinting by utilizing the fine-grained channel state information (CSI) that can be obtained on commercial-off-the-shelf (COTS) network interface cards. These studies, however, use only the summation of the received signals to distinguish different positions, which limits their performance gain over the existing RSS-based methods. Our observations show that the distribution of CSI amplitude on individual subcarriers rather than the summation over all subcarriers can provide much finer-grained differentiation among different positions. In this paper, we propose a new localization method that exploits the distribution of CSI as the fingerprint of positions. Our approach makes better use of the frequency diversity with different subcarriers and the spatial diversity with multiple antennas, and thus effectively improves the localization accuracy. The Kullback-Laibler divergence is used to calculate the similarity between different fingerprints, based on which the best matched position is calculated in the localization phase. The experiment results obtained in two typical indoor environments demonstrate that, compared with the state-of-the-art approach, the proposed approach improves localization accuracy by 30%.

In this paper, a femtosecond laser line-scanning irradiation was used to induce the periodic surface microstructure on HgCdTe crystal. Low spatial frequency laser induced periodic surface structures of 650–770 nm and high spatial frequency laser induced periodic surface structures of 152–246 nm were respectively found with different scanning speeds. The evolution process from low spatial frequency laser induced periodic surface structures to high spatial frequency laser induced periodic surface structures is characterized by scanning electron microscope. Their spatial periods deduced by using a two-dimensional Fourier transformation partly agree with the predictions of the Sipe-Drude theory. Confocal micro-Raman spectral show that the atomic arrangement of induced low spatial frequency laser-induced structures are basically consistent with the crystal in the central area of laser-scanning line, however a new peak at 164 cm −1 for the CdTe-like mode becomes evident due to the Hg vaporization when strong laser ablation happens. The obtained surface periodic ripples may have applications in fabricating advanced infrared detector.

The new features of HTML5 greatly increase the convenience for both web developers and users, but they also bring new security threats. Although the web-security community has started to analyze the security threats brought by HTML5, little has been performed to address the security threats for the client-side applications. This paper studies security issues of two popular client-side primitives: WebSocket and Web Storage. The security threats concerned in this paper are private information stealth through WebSocket and cross-site scripting vulnerabilities caused by lacking of sanitization for WebSocket messages and Web Storage data. We analyze the unsafe data flows of these two HTML5 primitives in detail. Based on that, we present a threat detection tool called TD-WS, which can automatically detect the privacy leaks and the cross-site scripting vulnerabilities in WebSocket and Web Storage applications. The results show that TD-WS effectively detects the security threats of WebSocket and Web Storage applications. Copyright © 2016 John Wiley & Sons, Ltd.

Multipath transmission control protocol (MPTCP) allows a TCP connection to operate across multiple paths simultaneously and becomes highly attractive to support the emerging mobile devices with various radio interfaces and to improve resource utilization as well as connection robustness. The existing multipath congestion control algorithms, however, are mainly loss-based and prefer the paths with lower drop rates, leading to severe performance degradation in wireless communication systems, where random packet losses occur frequently. To address this challenge and improve the performance of MPTCP in wireless networks, this paper proposes a new mVeno algorithm, which makes full use of the congestion information of all the subflows belonging to a TCP connection in order to adaptively adjust the transmission rate of each subflow. Specifically, mVeno modifies the additive increase phase of Veno so as to effectively couple all subflows by dynamically varying the congestion window increment based on the receiving ACKs. The weighted parameter of each subflow for tuning the congestion window is determined by distinguishing packet losses caused by random error of wireless links or by network congestion. We implement mVeno in a Linux server and conduct extensive experiments both in test bed and in real WAN to validate its effectiveness. The performance results demonstrate that compared with the existing schemes, mVeno increases the throughput significantly, achieves load balancing, and can keep the fairness with regular TCP.

A broadband MoS2-based absorber composed of Ag rod/MoS2/dielectric/Ag is proposed in the visible band. The relative bandwidth is 65% for the absorption above 80%. The absorber also has the properties of polarization-independence and wide-angle absorption. Impedance matching theory is used to analyze the physical mechanism of the broadband absorption. By investigating the absorption property of each part of the absorber, it is found that the absorption is enhanced by introducing the two-dimensional material MoS2. The broadband absorber can be changed to be multiband absorber by changing the thickness of dielectric substrate. This structure provides a new perspective to enhance absorption in the visible band and has promising applications in solar cells.

Background Exposure to PM2.5 can stimulate the mucus secretion of airway, affecting the development of bronchial asthma. NF-κB signal pathway plays an important role in inflammation and dysimmunity, what may contribute to the mucus secretion. The present study was undertaken to explore the effect of NF-κB signal pathway on mucus secretion induced by PM2.5 in rats with bronchial asthma. Methods Fifty rats (25 males and 25 females) were divided randomly into the control group, ovalbumin asthmatic model group, asthma low-, middle- and high-dose groups (n = 10, 5 males and 5 females each group). The control group, ovalbumin asthmatic model group received physiological saline; the asthma low-, middle- and high-dose groups received 1.5, 7.5 and 37.5 mg/kg PM2.5 on saline, which instilled into the trachea at 2-day intervals for two doses. Lung histopathology was observed by HE staining. The mRNA levels of NF-κB family gens were detected with real time PCR. IκB-α protein expression levels were detected with Western blot. IL-1β, TNF-α and Muc5ac levels were detected by ELISA. Results Respiratory mucus secretion increased with increasing dose of PM2.5. Compared with healthy rats, the protein expression levels of IκB-α were significantly lower in the lung of asthmatic rats (p Conclusion Short-term exposure to a high concentration of PM2.5 could up-regulate the mRNA expression levels of NF-κB family genes, activate the NF-κB signal pathway, stimulate more IL-1β and mucus secretion in rats with bronchial asthma. NF-κB signal pathway may regulate the level of IL-1β, which could influence the mucus secretion induced by PM2.5 in asthmatic rats.

The contention based medium access method of 802.11 standards is a fundamental cause of poor downlink goodput and high latency over wireless networks, which makes it impossible to provide QoS guarantees. The intensive channel contention leads to the performance degradation. The problem exacerbates when the traffic asymmetry between the uplink and the downlink is present. While prior research works proposed various mechanisms to alleviate the issue, little was done to specifically address the appropriate parameter setting in a real world network. This study presents a way to obtain the appropriate access parameters that improves the performance of QoS applications over wireless networks. In particular, we propose AQEDCA, a traffic-aware minimum contention window adjustment algorithm. We validate our scheme by the extensive real world network tests and the results show that our scheme improves the downlink goodput up to 199.13%, decreases the latency up to 54.77%, and can achieve tight QoS guarantees as compared to the existing schemes.

Facial neural edema is the pathophysiological base of Bell's palsy. The middle cranial fossa approach is used to relieve the oppression of facial nerve at its most narrow course in the facial canal. In this research, the authors mainly discussed the internal auditory canal segment of facial nerve, completely in the shadow of the bony structure, which is inconvenient for transmastoid decompression. Therefore, the objective was to explore the definite position of the fundus meatus acustici interni from internal acoustic pore. Two hundred persons (age 22-60, 100 men and 100 women), presenting with healthy facial nerve, ear, and internal auditory canal, were investigated by computed tomography 3-dimensional reconstruction. Using statistical method to analyze, the authors obtained the definite position of the fundus meatus acustici interni, regarding the internal acoustic pore as the origin of coordinates. Our data provided more significant information for medical workers to improve the efficiency of operation and to prevent complications of surgery.

In recent years, many fingerprint-based localization approaches have been proposed, in which different features (e.g., received signal strength (RSS) and channel state information (CSI)) were used as the fingerprints to distinguish different positions. Although CSI-based approaches usually achieve higher accuracy than RSSI-based approaches, we find that the localization results of different approaches usually compensate with each other, and by fusing different features we can get more accurate localization results than using only single feature. In this paper, we propose a localization method that fusing different features by combining results of different localization approaches to achieve higher accuracy. We first select three most possible candidate positions from all the candidate positions generated by different approaches according to a newly defined metric called confidence degree, and then use the weighted average of them as the position estimation. When there are more than three candidate positions, we use a minimal-triangle principle to break the tie and select three out of them. Our experiments show that the proposed approach achieves median error of 0.5 m and 1.1 m respectively in two typical indoor environments, significantly better than that of approaches using only single feature.

Cognitive radio (CR) networks have an ample but dynamic amount of spectrum for communications. Communication rendezvous in CR networks is the process of establishing a control channel between radios before they can communicate. Designing a communication rendezvous protocol that can take advantage of all the available spectrum at the same time is of great importance, because it alleviates load on control channels, and thus further reduces probability of collisions. In this paper, we present ETCH, efficient channel-hopping-based MAC-layer protocols for communication rendezvous in CR networks. Compared to the existing solutions, ETCH fully exploits spectrum diversity in communication rendezvous by allowing all the rendezvous channels to be utilized at the same time. We propose two protocols, SYNC-ETCH, which is a synchronous protocol assuming CR nodes can synchronize their channel hopping processes, and ASYNC-ETCH, which is an asynchronous protocol not relying on global clock synchronization. Our theoretical analysis and ns-2-based evaluation show that ETCH achieves better performances of time-to-rendezvous and throughput than the existing work.

Image classification aims to automatically group a set of images into several categorizations, which is widely applied in scene categorization, image clustering. Lung cancer recognition can be achieved by using image classification technique, since there are distinct differences between healthy lung and sick lung images. In this paper, we propose lung cancer recognition based on image quality assessment, which can distinguish sick lung images from healthy lung images. First, our dataset is acquired using low-dose CT scan combined with full-mode iterative recombination (IMR). Then, we incorporate both low-level and high-level features to extract deep representation from obtained dataset. Specifically, our designed low-level features include color moment and texture feature, and CNN based method is leveraged for deep feature extraction. For reducing artifacts and noise of images, we assign quality score for each training image. And quality score and deep feature are fused to generate deep representation. Afterward, we propose a probabilistic model to learn the distribution of deep representation. Finally, lung cancer recognition can be achieved using learned model. We conduct comprehensive experiments and our proposed method is verified effective.

User access control in sensor networks defines a process of granting user an access right to the stored information. It is essential for future real sensor network deployment in which sensors may provide users with different services in terms of data and resource accesses. A centralized access control mechanism requires the base station to be involved whenever a user requests to get authenticated and access the information stored in the sensor node, which is inefficient, not scalable, and is exposed to many potential attacks along long communication paths. In this paper, we propose a distributed user access control under a realistic adversary model in which sensors can be compromised and user may collude. We split the access control into local authentication conducted by a group of sensors physically close to a user, and a light remote authentication based on the endorsement of the local sensors. We implement the access control protocols on a testbed of TelosB motes. Our analysis and experimental results show that our schemes are feasible for real access control requirements.

Symmetric cryptography has been mostly used in security schemes in sensor networks due to the concern that public key cryptography (PKC) is too expensive for sensor devices. While these schemes are efficient in processing time, they generally require complicated key management, which may introduce high memory and communication overhead. On the contrary, PKC-based schemes have simple and clean key management, but cost more computational time. The recent progress in PKC implementation, specially elliptic curve cryptography (ECC), on sensors motivates us to design a PKC-based security scheme and compare its performance with the symmetric-key counterparts. This paper proposes a practical PKC-based access control for sensor networks, which consists of pairwise key establishment, local access control, and remote access control. We have implemented both cryptographic primitives on commercial off-the-shelf sensor devices. Building the user access control as a case study, we show that PKC-based protocol is more advantageous than those built on symmetric cryptography in terms of the memory usage, message complexity, and security resilience. Meanwhile, our work also provides insights in integrating and designing PKC-based security protocols for sensor networks.

Postpneumonectomy bronchopleural fistulas (BPFs) remain difficult management problems associated with considerable morbidity and mortality. Traditional therapies included primary repair or delayed closure with the creation of an Eloesser cavity and tissue flap reinforcement. We describe an less

A 1 kHz femtosecond double-pulse sequence irradiation is used to study the temporal evolution of nanograting in fused silica by controlling the delay times and polarization combinations of two independent beams from a Mach–Zehnder interferometer. A lateral laser-scan experiment with speed at 5 µm s−1 and each pulse energy of 1 µJ is firstly performed with the delay time from sub-picosecond to 10 ps, and then the written nanostructures are systematically studied under a cross-polarized microscope because the intensity of birefringence signal nearly corresponds to optical retardance and development level of the induced nanograting. The trend shows that the induced nanogratings can continue developing with a decrease of delay time in the case of the linear polarization pulse arriving before. In another vertical laser-scan experiment at the same speed and pulse energy, the morphologies of nanogratings embedded in the lines are characterized by scanning electron microscope after mechanical polishing and chemical etching. The self-organized patterns have a commonly spatial period of 200–300 nm and the orientation is always perpendicular to the polarization of the first laser pulse, and the second pulse in each sequence seems to promote the as-formed nanograting developing further even if the polarized direction is different from the previous pulse. These new findings verify again that a localized memory effect can make positive feedback to reinforce the patterned nanostripes. In that process, the impact ionization from the seed electrons left by the first pulse excitation and the photoionization of self-trapped excitons with lower ionization threshold results in an increase of the re-excited carriers during the second pulse irradiation and the subsequent development of the as-formed nanograting. Our result provides further proofs for understanding the physical mechanism of nanograting strongly connection with the interplay on multiple ionization channels.

In this article, we present Microsearch, a search system suitable for embedded devices used in ubiquitous computing environments. Akin to a desktop search engine, Microsearch indexes the information inside a small device, and accurately resolves a user's queries. Given the limited hardware, conventional search engine design and algorithms cannot be used. We adopt Information Retrieval (IR) techniques for query resolution, and proposed a new space-efficient top- k query resolution algorithm. A theoretical model of Microsearch is given to better understand the trade-offs in design parameters. Evaluation is done via actual implementation on off-the-shelf hardware.

A body sensor network (BSN) is a network of sensors deployed on a person's body for health care monitoring. Since the sensors collect personal medical data, security and privacy are important components in a BSN. In this paper, we developed IBE-Lite, a lightweight identity-based encryption suitable for sensors in a BSN. We present protocols based on IBE-Lite that balance security and privacy with accessibility and perform evaluation using experiments conducted on commercially available sensors.

A typical sensor network application is to monitor objects, including wildlife, vehicles and events, in which information about an object is periodically sent back to the sink. Many times, the object needs to be protected for security reasons. However, an adversary can detect message flows and trace the message back to its source by moving in the reverse direction of the flows. This paper aims to maximize source location privacy, which is evaluated by the adversary's traceback time, by designing routing protocols that distribute message flows to different routes. First, we give the performance bound for any routing scheme. Then, we present our routing schemes, which maximize the adversary's average traceback time and achieve max-min traceback time given certain energy constraints. We then propose WRS, a suboptimal but practical privacy-aware routing scheme, and provide simulation results. Finally, we extend the discussion to an extreme adversary model, which allows the adversary to deploy an adversary sensor network to monitor the message routing activities. Accordingly, we propose a random schedule scheme to confuse the adversary. To reduce the message delivery time, we give an approximation algorithm for message routing.

Given the extremely limited hardware resources on sensor nodes and the inclement deploying environment, the adversary Denial-of-Service (DoS) attack becomes a serious security threat toward wireless sensor networks. Without adequate defense mechanism, the adversary can simply inundate the network by flooding the bogus data packets, and paralyze the partial or whole sensor network by depleting node battery power. Prior work on false packet filtering in sensor networks are mostly based on symmetric key schemes, with the concern that the public key operations are too expensive for the resource constrained sensors. Recent progress in public key implementations on sensors, however, has shown that public key is already feasible for sensors. In this paper, we present PDF, a Public-key based false Data Filtering scheme that leverages Shamir's threshold cryptography and Elliptic Curve Cryptography (ECC), and effectively rejects 100% of false data packets. We evaluate PDF by real world implementation on MICAz motes. Our experiment results support the conclusion that PDF is practical for real world sensor deployment.

Flooding is a fundamental file search operation in unstructured peer-to-peer (P2P) file sharing systems, in which a peer starts the file search procedure by broadcasting a query to its neighbors, who continue to propagate it to their neighbors. This procedure repeats until a time-to-live (TTL) counter is decremented to 0. Flooding can seriously limit system scalability, because the number of redundant query messages grows exponentially during the message propagation. Our study shows that more than 70 percent of the generated messages are redundant in a flooding with a TTL of 7 in a moderately connected Gnutella network. Existing efforts to address this issue have been focused on limiting the use of the flooding operation. We propose a new flooding scheme, called LightFlood, with the objective of minimizing the number of redundant messages and retaining a similar message-propagating scope as that of the standard flooding. In the scheme, each peer keeps track of the connectivities of every immediate and next indirect neighbor peers, which can be acquired locally. LightFlood identifies the neighbor with the highest connectivity and uses the link to that neighbor to form a suboverlay within the existing P2P overlay. In LightFlood, flooding is divided into two stages. The first stage is a standard flooding with a limited number of TTL hops, where a message can spread to a sufficiently large scope with a small number of redundant messages. In the second stage, message propagating is only conducted along the suboverlay, significantly reducing the number of redundant messages. Our analysis and simulation experiments show that the LightFlood scheme provides a low-overhead broadcast facility that can be effectively used in P2P search. For example, compared with standard flooding with seven TTL hops, we show that LightFlood with an additional two to three hops can reduce up to 69 percent of the flooding messages and retain the same flooding scope. We believe that LightFlood can be widely used as a core mechanism for efficient message broadcasting in P2P systems due to its near-optimal performance.

The current security models on mobile devices do not provide the level of protection required to handle sensitive data, such as protected healthcare information. In this paper, we introduce a Distributed Android Security Framework (DASF). DASF is a custom security framework for Android-based mobile devices that allows the enforcement of dynamic security policies on an application's privileges and sensitive data. DASF allows servers to dynamically impose security policies by using an application-layer message protocol implemented in the system. The security policies enforced by DASF enables system-wide privilege restrictions on untrustworthy applications and sensitive data. Ultimately, DASF allows organizations (i.e., data owners) to dynamically dictate the security policies enforced by the system, and retain control of the sensitive data they send to the device. We implement a prototype of DASF on Android Jellybean, and perform a security and performance evaluation. We show the DASF can defeat a number of security threats associated with using mobile devices in the healthcare industry, and imposes a reasonable performance overhead.

A distributed access control module in wireless sensor networks (WSNs) allows the network to authorize and grant user access privileges for in-network data access. Prior research mainly focuses on designing such access control modules for WSNs, but little attention has been paid to protect user's identity privacy when a user is verified by the network for data accesses. Often, a user does not want the WSN to associate his identity to the data he requests, particularly in a single-owner multi-user WSN. In this paper, we present the design, implementation, and evaluation of a novel approach, Priccess, to ensure privacy-preserving access control. In addition to the theoretical analysis that demonstrates the security properties of Priccess, this paper also reports the experimental results of Priccess in a network of Imote2 motes, which show the efficiency of Priccess in practice.

In this paper, we show through a simple secure symmetric key based protocol design and experiments the feasibility of secure data collection in a vehicular sensor networks. This protocol exhibits high speed data routing for sensor data collection through vehicles. The large communictaion and storage capacities of a vehicle and its mobility facilitates this high speed routing scheme compared with routing through hop-by-hop communication among sensors. We demonstrate that the protocol works in a realistic setting by collecting the real trace data through real implementation.

In this paper, we show through a simple secure symmetric key based protocol design and its implementation the feasibility of secure data collection in a vehicular sensor networks. We demonstrate that the protocol works in a realistic setting by collecting the real trace data through real implementation. Some of the key considerations are efficiency, deployability and security. The protocol does not safeguard against some of the techniques an adversary could deploy, such as jamming and deliberate battery-draining.

While symmetric-key schemes are efficient in processing time for sensor networks, they generally require complicated key management, which may introduce large memory and communication overhead. On the contrary, public-key based schemes have simple and clean key management, but cost more computational time. The recent progress of elliptic curve cryptography (ECC) implementation on sensors motivates us to design a public-key scheme and compare its performance with the symmetric-key counterparts. This paper builds the user access control on commercial off-the-shelf sensor devices as a case study to show that the public-key scheme can be more advantageous in terms of the memory usage, message complexity, and security resilience. Meanwhile, our work also provides insights in integrating and designing public-key based security protocols for sensor networks.

Hardware advances will allow us to embed small devices into everyday objects such as toasters and coffee mugs, thus naturally form a wireless object network that connects the object with each other. This paper presents Snoogle, a search engine for such a network. Snoogle uses information retrieval techniques to index information and process user queries, and compression schemes such as Bloom filters to reduce communication overhead. Snoogle also considers security and privacy protections for sensitive data. We have implemented the system prototype on off-the-shelf sensor motes, and conducted extensive experiments to evaluate the system performance.

A body sensor network (BSN), is a network of sensors deployed on a person's body, usually for health care monitoring. Since the sensors collect personal medical data, security and privacy are important components in a body sensor network. At the same time, the collected data has to readily available in the event of an emergency. In this paper, we present IBE-Lite, a lightweight identity-based encryption suitable for sensors, and developed protocols based on IBE-Lite for a BSN.

In this paper, we present Microsearch, a search system suitable for small devices used in ubiquitous computing environments. Akin to a desktop search engine, Microsearch indexes the information inside a small device, and accurately resolves user queries. Given the very limited hardware resources, conventional search engine designs and algorithms cannot be used. We adopt information retrieval techniques for query resolution, and propose a space efficient algorithm to perform top-k query on limited hardware resources. Finally, we present a theoretical model of Microsearch to better understand the tradeoffs in system design parameters. By implementing Microsearch on actual hardware for evaluation, we demonstrate the feasibility of scaling down information retrieval systems onto very small devices.

This paper considers the performance problem of VoIP over 802.11e WLANs caused by the unfairness between uplink and downlink as well as the inefficient EDCA. A novel medium access control scheme named BEDCA (Balanced EDCA) is presented, which provides service differentiation between the access point (AP) and the mobile stations (STAs) to enhance VoIP capacity. In BEDCA, the expression of AP's contention window is obtained which is a relative constant value independent of the participating STAs. The minimum contention window of the STAs is traffic-aware based on the proposed algorithm. The performance improvement of BEDCA is verified through intensive simulations and the results show the capacity improvement of 82.1% compared to EDCA.

We report our implementation of the RSA and ECC public-key cryptosystem on Berkeley Motes. We detail the implementation of 1024-bit RSA and 160-bit ECC cryptosystems on MICA mote sensors. We have achieved the performance of 0.79s for RSA public key operation and 21.5s for private operation, and 1.3s for ECC signature generation and 2.8s for verification. For comparison, we also show our new ECC implementation on TelosB motes with a signature time 1.60s and a verification time 3.30s. For the detailed description of the implementation, we refer to our technical report [13].

Access control in sensor networks is used to authorise and grant users the right to access the network and data collected by sensors. Different users have different access right due to the access restriction implicated by the data security and confidentiality. Even though symmetric-key scheme, which has been investigated extensively for sensor networks, can fulfil the requirement, public-key cryptography is more flexible and simple rendering a clean interface for the security component. Against the popular belief that a public key scheme is not practical for sensor networks, this paper describes a public-key implementation of access control in a sensor network. We detail the implementation of Elliptic Curve Cryptography (ECC) over primary field, a public-key cryptography scheme, on TelosB, which is the latest sensor network platform. We evaluate the performance of our implementation and compare with other implementations we have ported to TelosB.