Impractical Assumptions Research Articles

Accurate Rectifier Characterization and Improved Modeling of Constant Power Load Wireless Power Transfer Systems

Wireless power transfer (WPT) is an emerging technology deployed for a wide range of applications. Various design challenges need to be addressed at both circuit and system levels. However, the conventional modeling methods either require impractical assumptions or only focus on the coil-to-coil part. This article presents a system-level model for WPT applications, including all the critical interior blocks, such as power amplifier, transmitter coil, receiver coil, matching networks, and rectifier. An accurate characterization method is proposed to obtain the rectifier impedance as a realistic load condition. Then, through frequency harmonic analysis, the power capabilities along the power flow path are analytically derived for both the fundamental and higher order harmonic components. The system efficiency and the power loss at each block can be accurately estimated. As a result, this model can not only optimize the system performance, but also help improve the thermal design for WPT products. With the assistance of this improved model, a practical design methodology is introduced to optimize the system parameters. An experimental prototype is built to validate the proposed model and the design methodology. Good correlations are observed between the calculations and experiments in both the time-domain and frequency-domain results.

Respiration Tracking for People Counting and Recognition

Wireless detection of respiration rates is crucial for many applications. Most of the state-of-the-art solutions estimate breathing rates with the prior knowledge of crowd numbers as well as assuming the distinct breathing rates of different users, which is neither natural nor realistic. However, few of them can leverage the estimated breathing rates to recognize human subjects (also known as identity matching). In this article, using the channel state information (CSI) of a single pair of commercial WiFi devices, a novel system is proposed to continuously track the breathing rates of multiple persons without such impractical assumptions. The proposed solution includes an adaptive subcarrier combination method that boosts the signal-to-noise ratio (SNR) of breathing signals, and iterative dynamic programming and a trace concatenating algorithm that continuously tracks the breathing rates of multiple users. By leveraging both the spectrum and time diversity of the CSI, our system can correctly extract the breathing rate traces even if some of them merge together for a short time period. Furthermore, by utilizing the breathing traces obtained, our system can do people counting and recognition simultaneously. Extensive experiments are conducted in two environments (an on-campus lab and a car). The results show that 86% of average accuracy can be achieved for people counting up to four people for both cases. For 97.9% out of all the testing cases, the absolute error of crowd number estimates is within 1. The system achieves an average accuracy of 85.78% for people recognition in a smart home case.

A Virtual Assisted Controller for Biological Cell Transportation in a Dynamic Environment with Variable Field of View

Existing control methods for cell transportation with optical tweezers are generally implemented under the following assumptions: The obstacles are known in advance; and that the field of view (FOV) of the microscope is fixed during the transportation process. The unknown obstacles suspended outside FOV may float closely to the targeted cell, thereby always leading to failure of existing transportation methods. Fixed FOV also restricts the available manipulation space, thereby limiting the transportation within a small area. In this article, a novel control method that abandons the mentioned impractical assumptions is designed. The approach integrates the functions of cell transportation, cell maintenance, and obstacle avoidance. A series of virtual-assisted geometrical models, including “virtual obstacles,” “protecting ring,” and “time varying reference region” are proposed to avoid unknown dynamic obstacles. Simulations and experiments on long-distance cell transportation with variable FOV, in accordance with the actual situation, are successfully performed. Therefore, the effectiveness of the proposed control method is demonstrated.

Efficient Path Planning for a Mobile Sink to Reliably Gather Data from Sensors with Diverse Sensing Rates and Limited Buffers

Wireless sensor networks are vulnerable to energy holes, where sensors close to a static sink are fast drained of their energy. Using a mobile sink (MS) can conquer this predicament and extend sensor lifetime. How to schedule a traveling path for the MS to efficiently gather data from sensors is critical in performance. Some studies select a subset of sensors as rendezvous points (RPs). Non-RP sensors send data to the nearest RPs and the MS visits RPs to retrieve data. However, these studies assume that sensors produce data with the same speed and have no limitation on buffer size. When the two assumptions are invalid, they may encounter serious packet loss due to buffer overflow at RPs. In the paper, we show that the path planning problem is NP-complete and propose an efficient path planning for reliable data gathering (EARTH) algorithm by relaxing these impractical assumptions. It forms a spanning tree to connect all sensors and then selects each RP based on hop count and distance in the tree and the amount of forwarding data from other sensors. An enhanced EARTH (eEARTH) algorithm is also developed to further reduce path length. Both EARTH and eEARTH incur less computational overhead and can flexibly recompute new paths when sensors change sensing rates. Simulation results verify that they can find short traveling paths for the MS to collect sensing data without packet loss, as compared with existing methods.

Event-Triggered Leader-Following Consensus for Nonlinear Multiagent Systems Subject to Actuator Saturation Using Dynamic Output Feedback Method

This paper addresses the distributed event-triggered consensus problem for a class of nonlinear multiagent systems subject to actuator saturation. A new distributed event-based dynamic output feedback controller is put forward via the relative output measurements of neighboring agents. It removes the impractical assumptions that the controllers for agents have to update continuously and the observers embedded in agents have to share information with their neighbors, thus the energy consumptions of agents are significantly reduced. Two event-triggered strategies for the cases with and without continuous communication among neighboring agents are established. Sufficient conditions are derived in terms of matrix inequalities to guarantee the exponential leader-following consensus. The problem of designing a distributed event-triggered control law such that the domain of attraction for consensus errors is enlarged, formulated, and solved as an optimization problem with matrix inequality constraints. Simulation results are given to verify the effectiveness of the theoretical results.

Utilization-Based Scheduling of Flexible Mixed-Criticality Real-Time Tasks

Mixed-criticality models are an emerging paradigm for the design of real-time systems because of their significantly improved resource efficiency. However, formal mixed-criticality models have traditionally been characterized by two impractical assumptions: once \textit{any} high-criticality task overruns, \textit{all} low-criticality tasks are suspended and \textit{all other} high-criticality tasks are assumed to exhibit high-criticality behaviors at the same time. In this paper, we propose a more realistic mixed-criticality model, called the flexible mixed-criticality (FMC) model, in which these two issues are addressed in a combined manner. In this new model, only the overrun task itself is assumed to exhibit high-criticality behavior, while other high-criticality tasks remain in the same mode as before. The guaranteed service levels of low-criticality tasks are gracefully degraded with the overruns of high-criticality tasks. We derive a utilization-based technique to analyze the schedulability of this new mixed-criticality model under EDF-VD scheduling. During runtime, the proposed test condition serves an important criterion for dynamic service level tuning, by means of which the maximum available execution budget for low-criticality tasks can be directly determined with minimal overhead while guaranteeing mixed-criticality schedulability. Experiments demonstrate the effectiveness of the FMC scheme compared with state-of-the-art techniques.

Proximal quasi-Newton methods for regularized convex optimization with linear and accelerated sublinear convergence rates

A general, inexact, efficient proximal quasi-Newton algorithm for composite optimization problems has been proposed by Scheinberg and Tang (Math Program 160:495–529, 2016) and a sublinear global convergence rate has been established. In this paper, we analyze the global convergence rate of this method, in the both exact and inexact settings, in the case when the objective function is strongly convex. We also investigate a practical variant of this method by establishing a simple stopping criterion for the subproblem optimization. Furthermore, we consider an accelerated variant, based on FISTA of Beck and Teboulle (SIAM 2:183–202, 2009), to the proximal quasi-Newton algorithm. Jiang et al. (SIAM 22:1042–1064, 2012) considered a similar accelerated method, where the convergence rate analysis relies on very strong impractical assumptions on Hessian estimates. We present a modified analysis while relaxing these assumptions and perform a numerical comparison of the accelerated proximal quasi-Newton algorithm and the regular one. Our analysis and computational results show that acceleration may not bring any benefit in the quasi-Newton setting.

Compressive detection and localization of multiple heterogeneous events in sensor networks

This paper focuses on the comprehensive event detection and localization problem which efficiently detects not only the number and the position, but also the event signal strength of events in sensor networks. We consider the practical situation where multiple events may simultaneously occur, their signal with heterogeneous strength attenuates over distance and their signal propagation region may overlap. The problem becomes even more challenging when we get rid of the commonly made impractical assumptions, such as the oversimplified binary detection model, the awareness of the number and potential positions of future events, and the existing of super sensor nodes with unlimited sensing range. Inspired by spatially sparse event occurrences, we propose the efficient compressive sensing based approach called CED. Instead of collecting complete sensor readings, our self-driven and fully distributed measurement construction process makes only a small number of qualified measurements, enabling compressive sensing based data recovery. The distinguishing feature of our approach is that it requires no knowledge of, and is adaptive to, the number of occurred events which is changing over time. We have validated signal attenuation model of real-world events and implemented the proposed approach on a testbed of 36 TelosB motes. Testbed experiments and simulation results jointly demonstrate that our approach can achieve high detection rate with event occurred grids while incurring modest transmission overhead.

A Billion-Scale Approximation Algorithm for Maximizing Benefit in Viral Marketing

Online social networks have been one of the most effective platforms for marketing and advertising. Through the “world-of-mouth” exchanges, so-called viral marketing, the influence and product adoption can spread from few key influencers to billions of users in the network. To identify those key influencers, a great amount of work has been devoted for the influence maximization ( IM ) problem that seeks a set of $k$ seed users that maximize the expected influence. Unfortunately, IM encloses two impractical assumptions: 1) any seed user can be acquired with the same cost and 2) all users are equally interested in the advertisement. In this paper, we propose a new problem, called cost-aware targeted viral marketing ( CTVM ), to find the most cost-effective seed users, who can influence the most relevant users to the advertisement. Since CTVM is NP-hard, we design an efficient $(1- 1/\sqrt {e}-\epsilon )$ -approximation algorithm, named Billion-scale Cost-award Targeted algorithm (BCT), to solve the problem in billion-scale networks. Comparing with IM algorithms, we show that BCT is both theoretically and experimentally faster than the state-of-the-arts while providing better solution quality. Moreover, we prove that under the linear threshold model, BCT is the first sub-linear time algorithm for CTVM (and IM ) in dense networks. We carry a comprehensive set of experiments on various real-networks with sizes up to several billion edges in diverse disciplines to show the absolute superiority of BCT on both CTVM and IM domains. Experiments on Twitter data set, containing 1.46 billions of social relations and 106 millions tweets, show that BCT can identify key influencers in trending topics in only few minutes.

A method for constructing the Composite Indicator of business cycles based on information granulation and Dynamic Time Warping

Composite indicators of business cycles play a paramount role in the analysis of macroeconomy, which provide decision makers with much meaningful information. This paper develops a novel constructing method of the business cycle composite indicator based on information granulation and Dynamic Time Warping (DTW), which not only takes the most important indicator real Gross Domestic Product (GDP) into account but avoids some impractical assumptions in dynamic factor models. First, the quarterly real GDP sequence is divided into information granules by the principle of justifiable granularity. Next, monthly coincident indicators are split into corresponding segments relying on the information granules of real GDP, and DTW is applied to measure the similarity between monthly and quarterly segments. The weights are derived by normalizing reciprocals of the above distance values. Finally, the monthly composite indicator of business cycles is obtained by taking a weighted cross-section average of those monthly coincident indicators. The numerical experiment reveals that the composite indicator established by the proposed method can reflect the dynamics of business cycles and accurately catch the turning points in business cycles.

Closure to “Dispute Resolution Strategy Selection” by Thomas Haugen and Amarjit Singh

The authors are appreciative of the suggestions by the discussers. However, an example application by them using fuzzy set theory is lacking, which would have helped reinforce their position, if possible. At present, their discussion is purely theoretical, and possibly, hypothetical. Next, the authors believe that the uncertainties associated with the adoption of game theory and fuzzy sets are also substantive, such that the discussers are unable to certainly prove that uncertainties in strategy selection are eliminated using game theory or fuzzy set theory. In addition, game theory is so heavily dependent on sampling unreliability and stochastic operations and approximations that the output is not always robust and credible. Fuzzy logic is still largely considered theoretical, despite the heavy research and advances made in it, and is not ready yet for use in practical decision making in all cases owing to its complexity and impractical assumptions. Various basic assumptions belie fuzzy set theory. In the dynamic nature of management systems, the crisp or fuzzy assumption of the bivalent condition of membership may be good enough for mathematical intellectual applications, but in real life, elements may belong to multiple sets, or could belong now to a specific set but not belong later, which the single fuzzy set cannot model. In addition, varying conditions of continuity and monotonicity also render the applications of fuzzy set theory inapplicable. Furthermore, the authors wish to state that even though some of the systems used by them rely on subjective input, such subjective input is not altogether arbitrary. First, this subjective input is obtained from those who have track records behind them. Next, the data are run through objective checking such as the analytic hierarchy process (AHP) and pairwise analysis, which tend to normalize the data input, if not eliminate uncertainty. Certainly, the numerical checks serve to eliminate severely asymmetric data, and so place upper and lower bounds on the data that can be accepted for input to the system. However, when objective data is hard to come by—and this happens often—mathematical techniques such as fuzzy set theory or game theory are unable to perform effectively, despite claims to the contrary. In such cases, it is well known that qualitative analysis provides great context, applicability, and reliability. Finally, it must be understood that in an industry already plagued by too many disputes, a simple and easy-to-use method is usually preferable compared to heavily mathematical methods. Even engineers in the construction industry do not understand fuzzy set theory, let alone apply them, not to mention that architects and attorneys working in the construction area would have little knowledge of fuzzy set theories. In contrast, the method proposed by the authors can be much more simply applied by engineers engaged in the construction industry. All that said, the authors encourage the discussers to actually develop new techniques for strategy selection and take the science of this to the next level.

Research on convertible bond pricing efficiency based on nonparametric fixed effect panel data model

Purpose – The purpose of this paper is to study the pricing efficiency of convertible bonds and arbitrage opportunities between the convertible bonds and the underlying stocks thus improve market efficiency. Design/methodology/approach – Using nonparametric fixed effect panel data model, the authors build pricing model of convertible bonds and obtain fitted value for them. Then the authors constructs simultaneous confidence band for the smooth function to identify mispricing and study the pricing efficiency and arbitrage opportunities of convertible bonds. Findings – Result shows, convertible bonds’ prices largely depend on stock prices. Pricing efficiency does not improve during the past few years as there are quite a few trading opportunities. Arbitrage opportunities increase as the stock prices approach it maxima, and selling opportunities for convertible bonds surpass buying opportunities which indicates that investors use market neutral strategies to arbitrage. Pricing efficiencies varies a lot and it is affected by the features of the stocks and convertible bonds. Index stocks eligible for margin trading with high liquidity enjoy higher pricing efficiency. Research limitations/implications – The study does not take into account trading cost and risk management measures. Practical/implications – Arbitrage between the underlying and the convertible bonds is profitable and contributes to pricing efficiency therefore should be encouraged. The regulator should pay attention to the extreme mispricing of the underlying and convertible bonds which cannot be corrected by the market as there might be manipulation. Originality/value – Since traditional pricing methods are based on the framework of non-arbitrage equilibrium with the assumption of balanced and perfect market, there are many restrictions in the pricing process and the practical utility is somewhat limited, and the impractical assumptions lead to model risk. This study uses nonparametric regression to study the pricing of convertible bonds thus circumvents the problem of model risk. Simultaneous confidence band for smooth function identifies mispricing and explicitly reflects the variation of pricing efficiency as well as signalizes trading opportunities. Application of nonparametric regression and simultaneous confidence band in derivative pricing is advantageous in accuracy and simplicity.

A Novel Coding Scheme for Secure Communications in Distributed RFID Systems

Privacy protection is the primary concern when RFID applications are deployed in our daily lives. Due to the computational power constraints of passive tags, non-encryption-based singulation protocols have been recently developed, in which wireless jamming is used. However, the existing private tag access protocols without shared secrets rely on impractical physical layer assumptions, and thus they are difficult to deploy. To tackle this issue, we first redesign the architecture of RFID system by dividing an RF reader into two different devices, an RF activator and a trusted shield device (TSD). Then, we propose a novel coding scheme, namely Random Flipping Random Jamming (RFRJ), to protect tags’ content. Unlike the past work, the proposed singulation protocol utilizes only the physical layer techniques that are already implemented. Analyses and simulation results validate our distributed architecture with the RFRJ coding scheme, which defends tags’ privacy against various adversaries including the random guessing attack, correlation attack, ghost-and-leech attack, and eavesdropping.

Optimal bandwidth allocation for hybrid Video-on-Demand streaming with a distributed max flow algorithm

Video-on-Demand (VoD) streaming counts for the largest share of Internet traffic, and commonly relies on P2P–CDN hybrid systems. In such hybrid systems, a peer’s upload bandwidth utilization is critical to P2P mode, thus the bandwidth allocation algorithm is important to the performance of VoD streaming. The current research either makes impractical assumptions, or is inefficient in real scenarios. Meanwhile some industrial practice such as additive-increase/multiplicative-decrease (AIMD) heuristic, although effective in practice, lacks theoretical foundation. This paper develops an optimal bandwidth allocation algorithm for hybrid VoD streaming. Specifically, we propose a novel Demand Driven Max-Flow Formulation, which treats each peer’s bandwidth demand as the flow commodity. The proposed distributed Free-for-All Push–Lift algorithm can solve the formulation in each peer, and is free of any lock, shared memory and atomic operation. Following the theoretical analysis, we implement the algorithms in real-world VoD streaming systems. Through extensive evaluations we demonstrate that our approach can provide high-quality bandwidth allocation for hybrid VoD systems.

Pavement damage cost estimation: a synthesis of past research

In order to generate the funds needed for preserving highway infrastructure, highway agencies impose various types of fees on road users. For purposes of efficiency and equity, user fees must reflect appropriately the share of pavement damage occasioned by each vehicle class. This paper presents a review of the state of practice of pavement damage cost estimation, identifies the drawbacks in past approaches and makes suggestions for enhancement. The paper explains how past highway pavement damage cost estimation studies were plagued with a number of debilitating limitations, ranging from the lack of appropriate data or unrealistic or impractical assumptions, to inadequate consideration of criteria that influence such costs. The paper demonstrates that, for the equitable and efficient estimation of pavement damage cost, the methodology used should be comprehensive. In other words, it should clearly distinguish between strength-driven and capacity-driven expenditures; make use of relevant historical agency cost and traffic data spanning a sufficient length of time; incorporate realistic highway agency maintenance, rehabilitation, and reconstruction practices; and recognise that pavements in any network are not uniform but rather have different ages, surface types and functional classes. Other desirable features of a pavement damage cost estimation study include the use of an appropriate road use measure, due consideration of all the relevant costs associated with pavement damage and incorporation of the effectiveness of maintenance and rehabilitation treatments.

Enhancing Cooperation in MANET Using the Backbone Group Model (An Application of Maximum Coverage Problem)

MANET is a cooperative network in which every node is responsible for routing and forwarding as a result consumes more battery power and bandwidth. In order to save itself in terms of battery power and bandwidth noncooperation is genuine. Cooperation can be enhanced on the basis of reduction in resource consumption by involving a limited number of nodes in routing activities rather than all. To get accurate selection of nodes to define a backbone several works have been proposed in the literature. These works define a backbone with impractical assumptions that is not feasible for MANET. In this paper we have presented the Backbone Group (BG) model, which involve the minimum number of nodes called BG in routing activities instead of all. A BG is a minimal set of nodes that efficiently connects the network. We have divided a MANET in terms of the single hop neighborhood called locality group (LG). In a LG we have a cluster head (CH), a set of regular nodes (RNs) and one or more border nodes (BNs). The CHs are responsible for the creation and management of LG and BG. The CHs use a BG for a threshold time then switches to another BG, to involve all nodes in network participation. The proposed model shows its effectiveness in terms of reduction in routing overhead up to a ratio (n2: n2/k) where k is the number of LGs.

Why Market Timing Makes Sense In China:Re-Examining Local Investor Behavior

In October 2000, when, during a televised interview, the renowned economist and long-standing capital markets advocate Wu Jinglian called the Chinese A-share market a “casino,” the public outcry was immediate. The early responses focused on the need for improved market regulation and transparency, but a few prominent market participants and academics highlighted a more fundamental problem: A-share investors’ stubborn reliance on market timing strategies to generate return. The persistence of this latter issue is why today—despite nearly 15 years of regulatory development and investor education—the gambling metaphor still finds traction. For overseas investors, this type of speculative investor behavior only reinforces the already robust investment case for China’s capital markets. The success of China’s f irst wave of reforms has bolstered confidence that the positive trends in consumer spending, urbanization, and productivity will ultimately be ref lected in higher share prices. Financial liberalization will introduce new f lows into the markets and support higher valuations. Further, according to J.P. Morgan Asset Management [2014], current valuations are still attractive: the market’s average price/forward earnings ratio in the third quarter of 2004 was less than 10x, and the 10-year average of a composite valuation metric evaluating price versus forward earnings, book value, historical cashf low, and historical dividends, respectively, was close to one standard deviation behind the long-term global market average. China’s interbank f ixed income market is still in a relatively nascent stage, but recent long maturity corporate issuance, newly introduced regulation to improve credit risk transparency, and the development of new securitized bonds—including the planned launch of mortage-backed securities—offer foreign investors the opportunity to benef it from unprecedented structural reforms. Recent efforts to liberalize interest rates have already had a positive impact on f ixed income market liquidity, and China’s current policy real yield remains among the highest in the world. Despite this positive long-term outlook for China’s capital markets, overseas investors would benefit from a close examination of domestic speculative investment behavior, particularly in China’s A-share market. Market timing—in simple practice, predicting the outperformance of stocks relative to bonds—is still a vexing topic in financial market theory. In the absence of persistent, superior forecasting skill, market timing is considered irrational behavior. Further, devising a method to assess this forecasting skill is fraught with difficulty and contingent on a number of impractical assumptions. Yet neither these impediments nor the long-term poor performance of onshore market timing strategies have deterred China A-share investors. In China, at least, this activity, albeit “irrational,” clearly must have some rationale. The seminal work by Merton [1981] on evaluating asset manager total performance proves relevant here. Merton argues that a pure market timing strategy can be regarded as a long position in a zero-cost European call option on the market portfolio, with a strike price that aligns to the drift path of the risk-free rate. This analogy Why Market Timing Makes Sense In China: Re-Examining Local Investor Behavior

Robust Decision Feedback Equalizer for OFDM System under Severe ISI Channel

Inter-symbol interference (ISI) problem is inevitable when the guard interval (GI) is shorter than the delay spread (DS) for an orthogonal frequency division multiplexing (OFDM) system. Iterative techniques have been proposed to overcome such a problem. However, most of existing algorithms are not efficient for an OFDM system with a small GI working under the channel with a large DS. Especially in the case of the DS spans a longer time than the half of the OFDM symbol duration. On the other hand, conventional algorithms, which can reduce the effects of the severe ISI, often employ several impractical assumptions to support the conclusions. In this paper, we present a robust decision feedback equalizer (DFE) for the OFDM system to overcome the severe ISI problem. The proposed DFE removes the ISI in a same manner as the residual inter-symbol interference cancellation (RISIC) algorithm. However, the inter-carrier interference (ICI) is reduced via cyclicity removal instead of the cyclicity restoration used in the conventional algorithms. The link-level simulation (LLS) results indicate that our proposed DFE scheme can dramatically improve the BER performance when the DS spans longer than the half of ODFM symbol duration.

Incremental Learning in Human Action Recognition Based on Snippets

In this paper, we present a systematic framework for recognizing human actions without relying on impractical assumptions, such as processing of an entire video or requiring a large look-ahead of frames to label an incoming video. As a secondary goal, we examine incremental learning as an overlooked obstruction to the implementation of reliable real-time recognition. Assuming weak appearance constancy, the shape of an actor is approximated by adaptively changing intensity histograms to extract pyramid histograms of oriented gradient features. As action progresses, the shape update is carried out by adjustment of a few blocks within a tracking window to closely track evolving contours. The nonlinear dynamics of an action are learned using a recursive analytic approach, which transforms training into a simple linear representation. Such a learning strategy has two advantages: 1) minimized error rates, and significant savings in computational time; and 2) elimination of the widely accepted limitations of batch-mode training for action recognition. The effectiveness of our proposed framework is corroborated by experimental validation against the state of the art.

Multipath Transmission for Wireless Internet Access--From an End-to-End Transport Layer Perspective

With the growing demand of Internet services, network operators have put significant efforts to improve network error resilience and efficiency. Since there exist different wired/wireless technologies for Internet access such as digital subscriber line (DSL), Ethernet, and worldwide interoperability for microwave access (WiMax), a mobile host can use multiple access networks simultaneously with multipath transmission. Taking the advantage of heterogeneous environment, multipath transmission through the Internet can improve service reliability and network flexibility. Ensuring a reliable end-to-end connection oriented communication with satisfactory quality of service (QoS) and maintaining congestion control and flow control are the main responsibilities of the Transmission Control Protocol (TCP), the dominant transport layer protocol in the Internet. In this paper, we survey the state-of-the-art of multipath transmission techniques for QoS provisioning in wireless Internet access, focusing on the end-to-end transport layer protocols. The main challenges for the design of multipath TCP are reviewed, and the existing transport layer congestion control schemes are categorized. Multipath TCP and stream control transmission protocol (SCTP)-based transport layer protocols are discussed, and their limitations and/or impractical assumptions are addressed. Open research issues on the development of an effective, efficient, and practical multipath TCP protocol are summarized.