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Colocating workloads are commonly used in datacenters to improve server utilization. However, the unpredictable application performance degradation caused by the contention for shared resources makes the problem difficult and limits the efficiency of this approach. This problem has sparked research in hardware and software techniques that focus on enhancing the datacenters’ isolation abilities. There is still lack of a comprehensive benchmark suite to evaluate such techniques. To address this problem, we present SDCBench, a new benchmark suite that is specifically designed for workload colocation and characterization in datacenters. SDCBench includes 16 applications that span a wide range of cloud scenarios, which are carefully selected from the existing benchmarks using the clustering analysis method. SDCBench implements a robust statistical methodology to support workload colocation and proposes a concept of latency entropy for measuring the isolation ability of cloud systems. It enables cloud tenants to understand the performance isolation ability in datacenters and choose their best-fitted cloud services. For cloud providers, it also helps them to improve the quality of service to increase their revenues. Experimental results show that SDCBench can simulate different workload colocation scenarios by generating pressures on multidimensional resources with simple configurations. We also use SDCBench to compare the latency entropies in public cloud platforms such as Huawei Cloud and AWS Cloud and a local prototype system FlameCluster-II; the evaluation results show FlameCluster-II has the best performance isolation ability over these three cloud systems, with 0.99 of experience availability and 0.29 of latency entropy.

Latency to end-users and regulatory requirements push cloud providers to operate many datacenters all around the globe to host their cloud services. An emerging problem under such geo-distributed architecture is to assign each user request to an appropriate datacenter to benefit both cloud providers (e.g., low bandwidth cost) and end-users (e.g., low latency)-known as request allocation. However, prior request allocation solutions have significant limitations: they either focus only on optimizing the benefits for one entity (e.g., providers or users), or ignore some practical yet indispensable factors (e.g., heterogeneous latency requirements of different users and diverse per unit bandwidth cost among different datacenters) when optimizing benefits for both entities. In this paper, we study the problem of minimizing the total bandwidth cost for cloud service providers while guaranteeing the latency requirement for end-users. Specifically, we formulate an integer programming with consideration of the diversities in both the delay of requests and per unit bandwidth cost of datacenters. To efficiently and practically solve this problem, we first relax the integer programming into a continuous convex optimization and then take the advantages of random sampling to enforce the solution to be a feasible one for the original integer programming. We have conducted rigorous theoretical analysis to prove that our algorithm can provide a considerable good competitive ratio. Extensive simulations demonstrate that our proposed algorithm can reduce the total bandwidth cost by 30% while guaranteeing the latency requirements of all requests, as compared to conventional methods.

Missing tag incidents are common in RFID-enabled supply-chain and warehousing scenarios due to cargo theft and employee error operations, which may lead to serious economic losses or potential safety hazards. On the premise of ensuring the accuracy of missing tag detection, this paper aims to improve the time efficiency in an integrated RFID system. Unlike prior work focusing on detecting missing items from a large number of homogeneous tags that are monitored by a single reader, one integrated RFID system possesses multiple readers to communicate with the heterogeneous tags, which have different categorical attributes. In addition, the prior work required repeating the execution several times to capture the missing tags in assorted categories, which is of low time efficiency. Thus, a protocol called Multi-reader Missing Tag Detection (MMTD) is proposed to capture the missing tag quickly and reliably, which can detect missing tags from different categories in a parallel manner and is much more time-efficient than previous work. MMTD has two major advantages compared to prior work: (i) It leverages the knowledge of the spatial distribution of tags to divide up a difficult detection task into several lightweight tasks, which are shared by multiple readers. (ii) It personalizes the time frame of the reader based on the tag population to optimize the utilization of the communication channel. The final simulation results reveal that MMTD is the best in time-efficiency among the comparison protocols, and MMTD outperforms the other missing tag detection protocols by at least 1.5× in the Integrated RFID scenarios.

Background: Managing and recycling electronic waste (e-waste), while useful and necessary, has resulted in significant contamination of several environments in China. The area around Tianjin, China has become one of the world's largest e-waste disposal centers, where electronics are processed by manually disassembly or burning, which can result in serious exposure of workers to a multitude of toxicants. Objective: The present study assessed potential genomic damage in workers involved in recycling e-waste. Methods: To detect cytogenetic and DNA damage, chromosomal aberrations (CA), cytokinesis blocking micronucleus (CBMN) and the comet assay were performed. Concentrations of some trace elements, markers of oxidative stress and polychlorinated biphenyls (PCBs) in whole blood or serum were measured, and relationships among the markers described above, age, and duration of exposure were analyzed. The profiles of expression of genes in lymphocytes in peripheral blood were assessed to determine the status of the regulation of genes involved in genome stability. Results: Concentrations of 28 PCB congeners in the whole blood of the exposed group were significantly (P

Nearest neighbor search is a fundamental problem in various domains, such as computer vision, data mining, and machine learning. With the explosive growth of data on the Internet, many new data structures using spatial partitions and recursive hyperplane decomposition (e.g., k-d trees) are proposed to speed up the nearest neighbor search. However, these data structures are facing big data challenges. To meet these challenges, binary hashing-based approximate nearest neighbor search methods attract substantial attention due to their fast query speed and drastically reduced storage. Since the most notably locality sensitive hashing was proposed, a large number of binary hashing methods have emerged. In this paper, we first illustrate the development of binary hashing research by proposing an overall and clear classification of them. Then we conduct extensive experiments to compare the performance of these methods on five famous and public data sets. Finally, we present our view on this topic.

Military heterogeneous networks often suffer from node or edge failures due to attacks, which lead to whole network topology segmentation or even paralysis. Therefore, structural invulnerability is the major technical bottleneck that impedes the combat capability of military heterogeneous system. Nodes are mainly considered in current methods, with little attention paid to edges. As such, current methods cannot accurately evaluate the structural invulnerability of military heterogeneous networks. This paper presents a new method of evaluating the survivability of military heterogeneous networks, based on network structure entropy. A model of survivability is proposed based on the network irreversibility which considers not only the nodes but also the edges. The node criticality is calculated by the level flow betweenness. Edge criticality is put forward based on the edge correlation factor and information transmission efficiency. Simulation results show that this invulnerability measure has the characteristics of high sensitivity and high precision, which will provide a theoretical basis for designing and optimizing the structure of the military heterogeneous network.

For data analytics jobs running across geographically distributed datacenters, coflows have to go through the inter-datacenter network over relatively low bandwidth and high cost links. In this case, optimizing cost-performance tradeoffs for such coflows becomes crucial. Ideally, decreasing the coflow completion time (CCT) can significantly improve the network performance, meanwhile, reducing the transmission cost introduced by these coflows is another fundamental goal for datacenter operators. Unfortunately, minimizing both the CCT and the transmission cost are conflicting objectives that cannot be achieved concurrently. Prior methods have significant limitations when exploring such tradeoffs, because they either merely decrease the average CCT or reduce the transmission cost independently. In this paper, we focus on a cost-performance tradeoff problem for coflows running across the inter-datacenter network. Specifically, we formulate an optimization problem, so as to minimize a combination of both the average CCT and the average transmission cost. This problem is inherently hard to solve due to the unknown information of future coflows. Therefore, we present Lever, an online coflow-aware optimization framework, to balance these two conflicting objectives. Without any prior knowledge of future coflows, Lever has been proved to have a non-trivial competitive ratio in solving this cost-performance tradeoff problem. Results from large-scale simulations demonstrate that Lever can significantly reduce the average transmission cost, and at the same time, speed up the completion of these coflows, compared with the state-of-the-art solutions.

Due to the advent of the Internet, massively multiplayer online role-playing games (MMORPGs) have been enjoyed worldwide by many players simultaneously, and game publishers’ revenues have reached billions of dollars from subscriptions alone. Frequent updates (e.g., versioning) and new contents (e.g., quest system) are the typical strategies adopted by developers to keep MMORPG experiences fresh and attractive. What makes such strategies attractive and retains the interest of players in MMORPGs? This study focuses on one aspect of a popular MMORPG: the player’s experience of the quest systems of Final Fantasy XIV (FF14). The different quest systems were analyzed considering Bartle’s players’ classification, specifically for the explorers and achievers. From an information science perspective, such an analysis can be achieved via game refinement (GR) theory, which formulates the information of the game’s progression into a measurable model of game sophistication. On top of that, we used the concept of motion in mind, which was derived from concepts in physics. It maps game progression information to enable the possible quantification and approximation of players’ mental movements and affective experiences in the game. Based on the analysis of the collected data using the proposed measures of GR and motion in mind, the impact of regular updates on players in long-term games is discussed. Insights from the study provide guidance and suggestions for potential improvements in long-term game design.

Abstract Immunosuppressive medications, such as tacrolimus and mycophenolate mofetil, are commonly used for reducing the risk of organ rejection in receipts of allogeneic organ transplant. The optimal dosages of these drugs are required for preventing rejection and avoiding toxicity to receipts. This study aimed to identify the correlation between the expression profiling of genes involved in drug metabolism and the blood level of tacrolimus in liver transplant receipts. Sixty-four liver transplant receipts were enrolled in this retrospective study. Receipts were divided into low (2–5.9 ng/ml) and high (6–15 ng/ml) tacrolimus groups. Clinical assessment showed that the blood level of tacrolimus was inversely correlated with the liver function evaluated by blood levels of total bilirubin and creatinine. Compared to the high tacrolimus group, expression levels of six cytochrome P450 enzymes, CYP1A1, CYP2B6, CYP3A5, CYP4A11, CYP19A1, and CYP17A1 were significantly higher in the low tacrolimus group. The expression levels of these genes were negatively correlated with the tacrolimus blood level. Enzyme assays showed that CYP3A5 and CYP17A1 exerted direct metabolic effects on tacrolimus and mycophenolate mofetil, respectively. These results support clinical application of this expression profiling of genes in drug metabolism for selection of immunosuppressive medications and optimal dosages for organ transplant receipts.

Cloud storage systems frequently have a large user base that requires huge cloud resources. Sometimes, cloud devices become overloaded because of an imbalance in input/output (I/O) or space demand. How can data with different popularity be distributed over heterogeneous devices? The key to resolving this problem is to balance the workload of multi-dimension resources. A consistent hash-aware cloud storage system constitutes a good solution for data placement. It can achieve only 1-D balance, usually the balance of the space resource. However, it is not straightforward to obtain a balance of space, I/O, and other resources simultaneously. Many users have experienced the overloading of devices in these systems. We focus mainly on this problem in this paper. In this paper, we discuss the factors that cause the overload of devices that occurs in the hash-aware cloud. Furthermore, we design some schemes with three algorithms to facilitate the assignment of hybrid data of different size and popularity to the heterogeneous cloud. The system can reduce the probability of an overload occurring. Most systems do not easily accommodate the movement of data. However, we argue that relocating part of the necessary data is helpful. This relocation can achieve a balance of resource usage and use fewer resources, without the need for replicas. Our system can provide a better quality of service, because the imbalance in the usage of resources is reduced. We performed an evaluation using extensive simulations driven by real-world traces. We demonstrate that our system can effectively reduce the overload probability of devices in cloud storage systems.

Software Defined Networks (SDN) is an emerging network architecture. It is shown that SDN can be deployed in a variety of small size networks, such as personal area networks and local area networks. Nevertheless, the deployment of SDN in large scale networks, such as wide area networks, raises a lot of concerns. In these scenarios, SDN relies on traditional hop-by-hop forwarding scheme which may lead to performance degradation due to heavy control traffic incurred and flow tables over-consumption. Source routing can be utilized to reduce control traffic and flow table consumption by installing only one flow table entry in the ingress switch and encoding complete routing path of a particular flow in the packet header. However, it will lead to severe bandwidth overhead which may also degrade network performance. In this paper, we propose an efficient forwarding scheme called Arbitrary Jump Source Routing (AJSR), which makes use of MPLS-based source routing. AJSR aims to achieve a trade-off between the control traffic overhead and the bandwidth overhead by dividing the complete routing path of a particular flow into arbitrary length sections and distributing these sections at different switches along the flow's routing path. To find the most suitable flow entry placement scheme, we formulate the flow entry placement problem as an optimization problem and present a dynamic programming solution with the objective of maximizing the overall cost saving. Through simulation experiments, we find that AJSR can effectively achieve a trade-off between the control traffic overhead and the bandwidth overhead.

In recent years, demands for rich multimedia services over mobile networks have been soaring at a tremendous pace. Traditional dedicated networking equipment may not be able to efficiently support the phenomenal growth of the traffic load and user demand dynamics while consuming an unnecessarily large amount of energy resources. Recently, mobile content caching, whereby popular contents are cached inside the mobile front-haul and back-haul networks so that demands for these contents from users in proximity can be easily accommodated without redundant transmissions from the remote sources, has emerged as an efficient technique for multimedia content delivery. Mobile content caching is particularly suitable for fifth generation (5G) mobile systems that are being designed to incorporate advanced cloud computing technologies and network function virtualization techniques. Therefore, in this paper, we first propose the concept of “Caching-as-a-Service” (CaaS) based on cloud-based radio access networks, and virtualized evolved packet core, which provides the capability to cache anything at anytime, anywhere in the cloud-based 5G mobile systems to satisfy user demands from any service location with high elasticity and adaptivity, and to empower third-party service providers with flexible controllability and programmability. Then, we study the potential techniques related to the virtualization of caching, and discuss the technical details of virtualization and optimization of CaaS in 5G mobile networks. Some novel schemes for CaaS are proposed to target different mobile applications and services. We also explore new opportunities and challenges for further research.

Social networking has become part of our life in recent years, allowing users to converse and connect with people sharing similar interests in real world. However, networking via the social media suffers from serious privacy issues, and one of which is profile attribute leakage in friend discovery. While existing studies mainly focus on leveraging rich cryptographic algorithms to prevent privacy leak, we propose a novel perturbation-based private profile matching mechanism by mixing the private data with random noise to preserve privacy in this paper. In this paper, we consider the case where the profiles are fine-grained, meaning that each attribute is associated with a user-specific numerical value to indicate the level of interest. By carefully tuning the amount of information owned by each party, we guarantee that privacy is effectively preserved while the matching result of users' profiles can be cooperatively obtained. We first give an introduction to a basic scheme, then detail two improved ones by, respectively, taking collusion attack and verifiability into consideration. As no expensive encryption algorithms get involved, our methods are computationally efficient; thus they are more practical for real-world applications. Theoretical security analysis as well as comparison-based simulation studies are carried out to evaluate the performance of our designs.

Objective The benefits of IL-35 treatment have been verified in multiple animal models of diseases, while its influence on T cells immunity under normal condition still needs to be elucidated. The present study was designed to investigate the effects modulating IL-35 levels in vivo and in vitro on T cells, response and also the effects on T cells subsets in normal mice. Methods A plasmid pMSCV-IL-35-GFP carrying mouse linear IL-35 fragment with two subunits joint together was constructed and the heterodimer expression was confirmed. Normal mice were randomly divided into three groups and received an intravenous injection of PBS, pMSCV-GFP and pMSCV-IL-35-GFP respectively. After 72 h, spleen tissues and peripheral blood were harvested for following analysis. Meanwhile, splenic T cells were isolated and incubated with 10, 30, or 50 ng/mL recombinant IL-35 factor for 24 h with the addition of anti-CD3/CD28 in vitro. T-cell subsets were assessed by Fluorescence activated cell sorting (FACS) and related cytokines together with effector molecules were determined by real time PCR. Results Western blotting confirmed a 52 kDa band in the cell lysate of HEK 293T transducted with pMSCV-IL-35-GFP plasmid, indicating a successful expression of IL-35. Ebi3 and IL-12A, two subunits of IL-35, could be identified 72 h post DNA injection. IL-35 upregulation in vivo effectively inhibit CD4+ and CD8+ T cell proliferation and Th1 cytokine secretion. Effector molecules of CD8+ T cells were also remarkably suppressed. On the contrary, high level of IL-35 significantly induced CD4+ CD25+ Tregs and Th2 enhancement. The in vitro study provided similar results. Conclusion The results indicated Th1 and CD8+ T cell inhibition and Th2 and Tregs bias in the presence of IL-35 under a normal state which partly contributed to its therapeutic potential.

In this paper, a coefficient adaptive scoring method of molecular docking is presented to improve the docking accuracy with multiple available scoring functions. Based on force-field scoring function, we considered hydrophobic and deformation as well in the proposed method, Instead of simple combination with fixed weights, coefficients of each factor are adaptive in searching procedure. In order to improve the docking accuracy and stability, knowledge-based scoring function is used as another scoring factor. Genetic algorithm with the multi-population evolution and entropy-based searching technique with narrowing down space is used to solve the optimization model for molecular docking. To evaluate the method, we carried out a numerical experiment with 134 protein- ligand complexes of the publicly available GOLD test set. The results validated that it improved the docking accuracy over the individual force-field scoring. In addition, analyses were given to show the disadvantage of individual scoring model. Through the comparison with other popular docking software, the proposed method showed higher accuracy. Among more than 77% of the complexes, the docked results were within 1.0 Å according to Root- Mean-Square Deviation (RMSD) of the X-ray structure. The average computing time obtained here is 563.9 s.

Load balancing is effective in reducing network congestion and improving network throughput in wireless sensor networks (WSNs). Due to the fluctuation of wireless channels, traditional schemes achieving load balancing in WSNs need to maintain global or local congestion information, which turn out to be complicated to implement. In this paper, we design a flowlet switching based load balancing scheme, called EasyLB, by extending OpenFlow protocol. Flowlet switching is efficient to achieve adaptive load balancing in WSNs. Nevertheless, one tricky problem lies in determining the flowlet timeout value, δ . Setting it too small would risk reordering issue, while setting it too large would reduce flowlet opportunities. By formulating the timeout setting problem with a stationary distribution of Markov chain, we give a theoretical reference for setting an appropriate timeout value in flowlet switching based load balancing scheme. Moreover, non-equal probability path selection and multiple parallel load balancing paths are considered in timeout setting problem. Experimental results show that, by setting timeout value following the preceding theoretical reference, EasyLB is adaptive to wireless channel condition change and achieves fast convergence of load balancing after link failures.

Data prediction can improve the science of decision-making by making predictions about what happens in daily life based on natural law trends. Back propagation (BP) neural network is a widely used prediction method. To reduce its probability of falling into local optimum and improve the prediction accuracy, we propose an improved BP neural network prediction method based on a multi-strategy sparrow search algorithm (MSSA). The weights and thresholds of the BP neural network are optimized using the sparrow search algorithm (SSA). Three strategies are designed to improve the SSA to enhance its optimization-seeking ability, leading to the MSSA-BP prediction model. The MSSA algorithm was tested with nine different types of benchmark functions to verify the optimization performance of the algorithm. Two different datasets were selected for comparison experiments on three groups of models. Under the same conditions, the mean absolute error (MAE), root mean square error (RMSE), andmean absolute percentage error (MAPE) of the prediction results of MSSA-BPwere significantly reduced, and the convergence speed was significantly improved. MSSA-BP can effectively improve the prediction accuracy and has certain application value. [ABSTRACT FROM AUTHOR]

The optimal solutions for various cases of multimedia object placement for transparent data replication while reducing total access cost by integrating both transmission cost and transcoding cost are presented. The empirical results confirmed the consistent and noteworthy outperformance of the solution as compared to other solution with regards to all the performance metrics.

Environmentally persistent organic pollutant (POP) is the general term for refractory organic compounds that show long-range atmospheric transport, environmental persistence, and bioaccumulation. It has been reported that the accumulation of POPs could lead to cellular DNA damage and adverse effects of on metabolic health. To better understand the mechanism of the health risks associated with POPs, we conducted an evidence-based cohort investigation (n = 5,955) at the Jinghai e-waste disposal center in China from 2009 to 2016, where people endure serious POP exposure. And high levels of aging-related diseases, including hypertension, diabetes, autoimmune diseases, and reproductive disorders were identified associated with the POP exposure. In the subsequent molecular level study, an increased telomere dysfunction including telomere multiple telomere signals, telomere signal-free ends, telomere shortening and activation of alternative lengthening of telomeres were observed, which might result from the hypomethylated DNA modification induced telomeric repeat-containing RNA overexpression. Moreover, dysfunctional telomere-leaded senescence-associated secretory phenotype was confirmed, as the proinflammatory cytokines and immunosenescence hallmarks including interleukin-6, P16INK4a, and P14ARF were stimulated. Thus, we proposed that the dysfunctional telomere and elevated systemic chronic inflammation contribute to the aging-associated diseases, which were highly developed among the POP exposure individuals. [ABSTRACT FROM AUTHOR]

Today, many applications such as social network and biological network develop rapidly, the graph data will be expanded constantly on a large scale. Some classic methods can not effectively solve this scale of the graph data. In the reachability query, many technologies such as N-Hop, tree, interval labels, uncertain graph processing are emerging, they also solve a lot of questions about reachability query of graph. But, these methods have not put forward the effective solution for the new issues of the multiattribute constraints reachability on directed graph. In this paper, TCRQDG algorithm effectively solves this new problem. Firstly it optimizes the multiattribute constraints with decision making technology; secondly the algorithm achieves fast and accurate query by integrating with the Create virtual vertex expand, conditions filtering, cycles contraction, interval label and other technology. TCRQDG algorithm can not only effectively solve the new problem, but also provide technical support for multiple constraints optimization decisions of network transmission, transport and logistics, software testing and other applications. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the encoding/decoding designing and its performance analysis for a two-way relaying system. In the two-way relaying system, two source points broadcast their information to a group of relays in different phases. The relays lightly decode the received signals from them to estimate the information of two source points. An exclusive OR (XOR)-based network coding is then performed by each relay over two estimated binary sequences even though they contain errors. Each relay re-encodes the XORed information using concatenated convolutional codes and forwards the coded information to two source points. A joint decoding scheme which utilizes the correlation knowledge among the relayed information is proposed to reduce the energy consumption and to achieve well performance. Simulation results show that the proposed scheme can signi canlly reduce the energy consumptions and can achieve full diversity order. [ABSTRACT FROM AUTHOR]

Users' preferences, and consequently their ratings and reviews to items, change over time. Likewise, characteristics of items are also time-varying. By dividing data into time periods, temporal Recommender Systems (RSs) improve recommendation accuracy by exploring the temporal dynamics in user rating data. However, temporal RSs have to cope with rating sparsity in each time period. Meanwhile, reviews generated by users contain rich information about their preferences, which can be exploited to address rating sparsity and further improve the performance of temporal RSs. In this article, we develop a temporal rating model with topics that jointly mines the temporal dynamics of both user-item ratings and reviews. Studying temporal drifts in reviews helps us understand item rating evolutions and user interest changes over time. Our model also automatically splits the review text in each time period into interim words and intrinsic words. By linking interim words and intrinsic words to short-term and long-term item features, respectively, we jointly mine the temporal changes in user and item latent features together with the associated review text in a single learning stage. Through experiments on 28 real-world datasets collected from Amazon, we show that the rating prediction accuracy of our model significantly outperforms the existing state-of-art RS models. And our model can automatically identify representative interim words in each time period as well as intrinsic words across all time periods. This can be very useful in understanding the time evolution of users' preferences and items' characteristics. [ABSTRACT FROM AUTHOR]

This paper concerns the practically important problem of multi-category radio frequency identification (RFID) estimation: given a set of RFID tags, we want to quickly and accurately estimate the number of tags in each category. However, almost all the existing RFID estimation protocols are dedicated to the estimation problem on a single set, regardless of tag categories. A feasible solution is to separately execute the existing estimation protocols on each category. The execution time of such a serial solution is proportional to the number of categories, and cannot satisfy the delay-stringent application scenarios. Simultaneous RIFD estimation over multiple categories is desirable, and hence, this paper proposes an approach called simultaneous estimation for multi-category RFID systems (SEM). SEM exploits the Manchester-coding mechanism, which is supported by the ISO 18000-6 RFID standard, to decode the combined signals, thereby simultaneously obtaining the reply status of tags from each category. As a result, multiple bit vectors are decoded from just one physical slotted frame. Built on our SEM, many existing excellent estimation protocols can be used to estimate the tag cardinality of each category in a simultaneous manner. To ensure the predefined accuracy, we calculate the variance of the estimate in one round, as well as the variance of the average estimate in multiple rounds. To find the optimal frame size, we propose an efficient binary search-based algorithm. To address significant variance in category sizes, we propose an adaptive partitioning (AP) strategy to group categories of similar sizes together and execute the estimation protocol for each group separately. Compared with the existing protocols, our approach is much faster, meanwhile satisfying the predefined estimation accuracy. For example, with 20 categories, the proposed SEM+AP is about seven times faster than prior estimation schemes. Moreover, our approach is the only one whose normalized estimation time (i.e., time per category) decreases as the number of categories increases. [ABSTRACT FROM PUBLISHER]

With the increasing popularization of radio frequency identification (RFID) technology in the retail and logistics industry, RFID privacy concern has attracted much attention, because a tag responds to queries from readers no matter they are authorized or not. An effective solution is to use a commercially available blocker tag that behaves as if a set of tags with known blocking IDs are present. However, the use of blocker tags makes the classical RFID estimation problem much more challenging, as some genuine tag IDs are covered by the blocker tag and some are not. In this paper, we propose RFID estimation scheme with blocker tags (REB), the first RFID estimation scheme with the presence of blocker tags. REB uses the framed slotted Aloha protocol specified in the EPC C1G2 standard. For each round of the Aloha protocol, REB first executes the protocol on the genuine tags and the blocker tag, and then virtually executes the protocol on the known blocking IDs using the same Aloha protocol parameters. REB conducts statistical inference from the two sets of responses and estimates the number of genuine tags. Rigorous theoretical analysis of parameter settings is proposed to guarantee the required estimation accuracy, meanwhile minimizing the time cost and energy cost of REB. We also reveal a fundamental tradeoff between the time cost and energy cost of REB, which can be flexibly adjusted by the users according to the practical requirements. Extensive experimental results reveal that REB significantly outperforms the state-of-the-art identification protocols in terms of both time efficiency and energy efficiency. [ABSTRACT FROM PUBLISHER]

In large-scale radio frequency identification (RFID)-enabled applications, we sometimes only pay attention to a small set of key tags, instead of all. This paper studies the problem of key tag population tracking, which aims at estimating how many key tags in a given set exist in the current RFID system and how many of them are absent. Previous work is slow to solve this problem due to the serious interference replies from a large number of ordinary (i.e., non-key) tags. However, time-efficiency is a crucial metric to the studied key tag tracking problem. In this paper, we propose a singleton slot-based estimator, which is time-efficient, because the RFID reader only needs to observe the status change of expected singleton slots corresponding to key tags instead of the whole time frame. In practice, the ratio of key tags to all current tags is small, because key members are usually rare. As a result, even when the whole time frame is long, the number of expected singleton slots is limited and the running of our protocol is very fast. To obtain good scalability in large-scale RFID systems, we exploit the sampling idea in the estimation process. A rigorous theoretical analysis shows that the proposed protocol can provide guaranteed estimation accuracy to end users. Extensive simulation results demonstrate that our scheme outperforms the prior protocols by significantly reducing the time cost. [ABSTRACT FROM PUBLISHER]

Efficiency and fairness are the most important objective for the network design. Network utility maximum (NUM) has been stated to solve this problem. This optimal problem can maximize the sum of all the nodes' utility with the transmission rate of each node as the variable. The transmission rate is constrained by the link's capacity. It considers the tradeoff between the efficiency and fairness of the whole network. But when we consider this problem in the wireless sensor networks, according to the capacity of each link is varying with the time, it is impossible to correctly estimate it on-the-fly. So we propose a method to solve the NUM problem that does not require knowledge of each link's capacity in the wireless sensor networks. Our mechanism doesn't have the problem of wasting memory and can estimate the congestion condition more correctly when dealing with multi-hop nodes. Simulation results express that our method can maximize the utility of the senor network. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of Object Based Image Retrieval (OBIR) where the goal is to search for the images containing the same object shown in the query image. The state-of-the-art approaches of large scale OBIR are based on the bag of visual words model. In the bag of visual words model, k-means clustering is performed on the space of feature descriptors to build a visual vocabulary for vector quantization, thereby we can get an inverted file indexing for fast retrieval. However, traditional k-means clustering is difficult to scale to large vocabularies. Although the approximate k-means clustering algorithm and the product quantization(PQ) are proposed to address this problem, the information loss in these methods decreases the performance of OBIR. To reduce information loss, we propose a novel approach to build multiple visual dictionaries indexing for large scale OBIR in this paper. Differing from existing methods, the proposed approach includes three novel contributions. Firstly, we use multiple visual vocabularies built in multiple sub-spaces for vector quantization instead of a single visual vocabulary. Secondly, visual dictionary indexing is proposed, which is more discriminative than inverted file indexing. Thirdly, except for the TF-IDF weighting scheme, a new weighting scheme is introduced to compute the final scores of the relevance of an image to the query more accurately. To evaluate the proposed approach, we conduct experiments on public image datasets. The experimental results demonstrate very significant improvements over the state-of-the-art approaches on these datasets. [ABSTRACT FROM AUTHOR]