The current development of video quality assessment algorithms suffers from the lack of available video sequences for training, verification and validation to determine and enhance the algorithm's application scope. The Joint Effort Group of the Video Quality Experts Group (VQEG-JEG) is currently driving efforts towards the creation of large scale, reproducible, and easy to use databases. These databases will contain bitstreams of recent video encoders (H.264, H.265), packet loss impairment patterns and impaired bitstreams, pre-parsed bitstream information into files in XML syntax, and well-known objective video quality measurement outputs. The database is continuously updated and enlarged using reproducible processing chains. Currently, more than 70,000 sequences are available for statistical analysis of video quality measurement algorithms. New research questions are posed as the database is designed to verify and validate models on a very large scale, testing and validating various scopes of applications, while subjective assessment has to be limited to a comparably small subset of the database. Special focus is given on the principles guiding the database development, and some results are given to illustrate the practical usefulness of such a database with respect to the detailed new research questions.
In this paper, we posit that extension of SDN to support deeply and flexibly programmable, software-defined data plane significantly enhance SDN and NFV and their interaction in terms of (1) enhanced interaction between applications and networks, (2) optimization of network functions, and (3) rapid development of new network protocols. All of these benefits are expected to contribute to improving the quality of diversifying communication networks and services. We identify three major technical challenges for enabling software-defined data plane as (1) ease of programming, (2) reasonable and predictable performance and (3) isolation among multiple concurrent logics. We also promote application-driving thinking towards defining software defined data-plane. We briefly introduce our project FLARE and its related technologies and review four use cases of flexible and deeply programmable data plane.
Network traffic load usually differs significantly at different times of a day due to users' different time-preference. Network congestion may happen in traffic peak times. In order to prevent this from happening, network service providers (NSPs) can either over-provision capacity for demand at peak times of the day, or use dynamic time-dependent pricing (TDP) scheme to reduce the demand at traffic peak times. Since over-provisioning network capacity is costly, many researchers have proposed TDP schemes to control congestion as well as to improve the revenue of NSPs. To the best of our knowledge, all the studies on TDP schemes consider only the monopoly or duopoly NSP case. In our previous work, the duopoly NSP case has been studied with the assumption that each NSP has complete information of quality of service (QoS) of the other NSP. In this paper, an oligopoly NSP case is studied. NSPs try to maximize their overall revenue by setting time-dependent price, while users choose NSPs by considering their own time preference, congestion status in the networks and the price set by the NSPs. The interactions among NSPs are modeled as an oligopoly Bertrand game. Firstly, assuming that each NSP has complete information of QoS of all NSPs, a unique Nash equilibrium of the game is established under the assumption that users' valuation of QoS is uniformly distributed. Secondly, the assumption of complete information of QoS of all NSPs is relaxed, and a learning algorithm is proposed for NSPs to achieve the Nash equilibrium of the game. Analytical and experimental results show that NSPs can benefit from TDP scheme, however, not only the competition effect but also the incomplete information among NSPs causes revenue loss for NSPs under the TDP scheme.
In recent years, there has been growing interest in systems for sharing resources, which were originally used for personal purposes by individual users, among many unspecified users via a network. An example of such systems is a peer-to-peer (P2P) data storage system that enables users to share a portion of unused space in their own storage devices among themselves. In a recent paper on a P2P data storage system, the user behavior model was defined based on supply and demand functions that depend only on the storage space unit price in a virtual marketplace. However, it was implicitly assumed that other factors, such as unused space of storage devices possessed by users and additional storage space asked by users, did not affect the characteristics of the supply and demand functions. In addition, it was not clear how the values of parameters used in the user behavior model were determined. Therefore, in this paper, we modify the supply and demand functions and determine the values of their parameters by taking the above mentioned factors as well as the price structure of storage devices in a real marketplace into account. Moreover, we provide a numerical example to evaluate the social welfare realized by the P2P data storage system as a typical application of the modified supply and demand functions.
Video traffic occupies a major part of current mobile traffic. The characteristics of video traffic are dominated by the behavior of the video application users. This paper uses a state transition diagram to analyze the behavior of video application users on smart phones. Video application users are divided into two categories; keyword search users and initial screen users. They take different first action in video viewing. The result of our analysis shows that the patience of video application users depends on whether they have a specific purpose when they launch a video application or not. Mobile network operators can improve the QoE of video application users by utilizing the results of this study.
We develop a system for comprehensively evaluating the gaze motions of a person operating a small electronic device such as a PDA or tablet computer. When people operate small electronic devices, they hold the device in their hand and gaze at it. Their hand movements while holding the device are considered part of the movement involved in operating the device. Our measurement system uses a video camera image taken from behind the subject as a substitute for the view camera of an eye-tracking recorder. With our new system, it is also possible to measure the subject's gaze superimposed on the view image by directly inputting the display screen from a small electronic terminal or other display. We converted the subjects' head and hand movements into eye movements and we calculated the gaze from these values; we transformed the gaze coordinates into view image coordinates and superimposed each gaze on the view image. We examined this hand movement in relation to gaze movement by simultaneously measuring the gaze movement and hand movement. We evaluated the accuracy of the new system by conducting several experiments. We first performed an experiment testing gaze movement as the summation of head and eye movements, and then we performed an experiment to test the system's accuracy for measuring hand movements. From the result of experiments, less than approx. 6.1° accuracy was acquired in the horizontal 120° range and the perpendicular 90° range, and we found that the hand motions converted into the angle equivalent to gaze movement could be detected with approx. 1.2° accuracy for 5° and 10° hand movements. When the subjects' hand moved forward, the results were changed into the angle equivalent to gaze movement by converting the distance between the terminal and the subjects' eyes.
In this paper, the Quality of Experience (QoE) on Dynamic Adaptive Streaming based on HTTP (DASH) is researched. To study users' experience on DASH, extensive subjective tests are firstly designed and conducted, based on which, we research QoE enhancement in DASH and find that DASH ensures more fluent playback (less stall) than constant bitrate (CBR) streaming to promote users' satisfaction especially in mobile networks. Then we adopt two-way analysis of variance (ANOVA) tests in statistics to identify the effect of specific factors (segment bitrate, bitrate fluctuation pattern, and bitrate switching) that impair users' experience on DASH. The impairment functions are then derived for these influence factors based on the Primacy and Recency Effect, a psychological phenomenon that has been proved to exist in users' experience on DASH in this paper. And the final QoE evaluation model is proposed to provide high correlation assessment for QoE of DASH. The good performance of our QoE model is validated by the subjective tests. In addition, our QoE study on DASH is also applied for QoE management to propose a QoE-based bitrate adaptation strategy, which promotes users' experience on DASH more strongly than the strategy based on QoS.
With the rapid growth of high performance ICT (Information Communication Technologies) devices such as smart phones and tablet PCs, multitasking has become one of the popular ways of using mobile devices. The reasons users have adopted multitask operation are that it reduces the level of dissatisfaction regarding waiting time and makes effective use of time by switching their attention from the waiting process to other content. This is a good solution to the problem of waiting; however, it may cause another problem, which is the increase in traffic volume due to the multiple applications being worked on simultaneously. Thus, an effective method to control throughput adapted to the multitasking situation is required. This paper proposes a transmission rate control method for web browsing that takes multitasking behavior into account and quantitatively demonstrates the effect of service by two different field experiments. The main contribution of this paper is to present a service design process for a new transmission rate control that takes into account human-network interaction based on the human-centered approach. We show that the degree of satisfaction in relation to waiting time did not degrade even when a field trial using a testbed showed that throughput of the background task was reduced by 40%.
In this paper, we propose a new cross-layer scheme Cooperation between channel Access control and TCP Rate Adaptation (CATRA) aiming to manage TCP flow contention in multi-hop ad hoc networks. CATRA scheme collects useful information from MAC and physical layers to estimate channel utilization of the station. Based on this information, we adjust Contention Window (CW) size to control the contention between stations. It can also achieve fair channel access for fair channel access of each station and the efficient spatial channel usage. Moreover, the fair value of bandwidth allocation for each flow is calculated and sent to the Transport layer. Then, we adjust the sending rate of TCP flow to solve the contention between flows and the throughput of each flow becomes fairer. The performance of CATRA is examined on various multi-hop network topologies by using Network Simulator (NS-2).
Orthogonal frequency division multiplexing (OFDM) has great advantages of high spectrum efficiency and robustness against multipath fading. When the received signal is deeply suppressed by deep fading, path loss and shadowing, the received carrier power must be increased in order to avoid degrading communication quality and provide high reliability at the cost of lower system throughput. A repetition coding is very attractive in providing the high reliability with simple configuration and the low decoding complexity of maximal ratio combining. In order to analytically confirm the effectiveness of repetition coded OFDM systems, we theoretically analyze the effect of increasing the number of repetitions (diversity branches) and acquiring both time and frequency diversity gain, and then derive a closed-form equation of average bit error rate (BER) to easily but precisely evaluate the performance.
We consider a location-aware store-carry-forward routing scheme based on node density estimation (LA Routing in short), which adopts different message forwarding strategies depending on node density at contact locations where two nodes encounter. To do so, each node estimates a node density distribution based on information about contact locations. In this paper, we clarify how the estimation accuracy affects the performance of LA Routing. We also examine the performance of LA Routing when it applies to networks with homogeneous node density. Through simulation experiments, we show that LA Routing is fairly robust against the accuracy of node density estimation and its performance is comparable with Probabilistic Routing even in the case that that node density is homogeneous.
Cognitive radio ad hoc networks can be used to solve the problems of limited available spectrum and inefficient spectrum usage by adaptively changing their transmission parameters. Routing protocol design has a significant impact on the network performance. However, an efficient protocol that takes account of primary user flows and the long-term channel assignment issue in route selection is still missing. In this paper, we propose AODV-cog, a cognitive routing protocol for CSMA/CA ad hoc networks based on AODV. AODV-cog chooses a route by considering the effect on the primary users, available channel bandwidth and link reliability. AODV-cog also takes account of future channel utilization which is an important but underexplored issue. AODV-cog switches channels for secondary user flows when network congestion occurs. We use theoretical analysis and computer simulations to show the advantage of AODV-cog over existing alternatives.
Most recent work on cooperative spectrum sensing using cognitive radios has focused on issues involving the sensing channels and seemed to ignore those involving the reporting channels. Furthermore, no research has treated the effect of correlated composite Rayleigh-lognormal fading, also known as Suzuki fading, in cognitive radio. This paper proposes a technique for reuse of shadowed CRs, discarded during the sensing phase, as amplified-and-forward (AF) diversity relays for other surviving CRs to mitigate the effects of such fading in reporting channels. A thorough analysis of and a closed-form expression for the outage probability of the resulting cooperative AF diversity network in correlated composite Rayleigh-lognormal fading channels are presented in this paper. In particular, an efficient solution to the “PDF of sum-of-powers” of correlated Suzuki-distributed random variables using moment generating function (MGF) is proposed.
In this paper, we address the issue of interference alignment (IA) in a two-cell network and consider both inter-cell and intra-cell interferences. For cell one, a linear processing scheme is proposed to align the inter-cell interference to the same signal dimension space of intra-cell interference. For cell two, we propose a distributed interference alignment scheme to manage the interference from the nearby cell. We assume that the relay works in an amplify-and-forward (AF) mode with a half-duplex and MIMO relaying. We show that the composite desired and interfering signals aggregated over two time slots can be aligned such that the interfering signal is eliminated completely by applying a linear filter at the receiver. The precoding matrix of the relay is optimized jointly with the precoding matrix of the base station (BS). The number of data streams is optimized jointly for every user terminal (UT). The degree of freedom (DoF) performance of the proposed scheme as well as the conventional cooperation scheme are derived for multiple antennas at both base stations, relay station and user terminals. Simulation results show that the proposed alignment scheme can achieve a better DoF performance.
Information networks are an important infrastructure and their resources are shared by many users. In order to utilize their resources efficiently, they should be controlled to prevent synchronization of user traffic. In addition, fairness among users must be assured. This paper discusses the framework of transmission rate control based on chaos. There are two different characteristics that coexist in chaos. One is that the state in the future is extremely sensitive to the initial condition. This makes it impossible to predict the future state at a fine level of detail. The other is the structural stability of macroscopic dynamics. Even if the state is uncertain on the microscopic scale, state dynamics on the macroscopic scale are stable. This paper proposes a novel framework of distributed hierarchical control of transmission rate by interpreting the coexistence of chaos as microscopic fairness of users and macroscopic stable utilization of networks.
The spatial relations between sensors placed for target detection can be inferred from the responses of individual sensors to the target objects. Motivated by this fact, this paper proposes a method for estimating the location of sensors by using their responses to target objects. The key idea of the proposal is that when two or more sensors simultaneously detect an object, the distances between these sensors are assumed to be equal to a constant called the basic range. Thus, new pieces of proximity information are obtained whenever an object passes over the area in which the sensors are deployed. This information is then be aggregated and transformed into a two dimensional map of sensors by solving a nonlinear optimization problem, where the value of the basic range is estimated together. Simulation experiments verify that the proposed algorithm yields accurate estimates of the locations of sensors.
Computation in the brain is realized in complicated, heterogeneous, and extremely large-scale network of neurons. About a hundred billion neurons communicate with each other by action potentials called “spike firings” that are delivered to thousands of other neurons from each. Repeated integration and networking of these spike trains in the network finally form the substance of our cognition, perception, planning, and motor control. Beyond conventional views of neural network mechanisms, recent rapid advances in both experimental and theoretical neuroscience unveil that the brain is a dynamical system to actively treat environmental information rather passively process it. The brain utilizes internal dynamics to realize our resilient and efficient perception and behavior. In this paper, by considering similarities and differences of the brain and information networks, we discuss a possibility of information networks with brain-like continuing internal dynamics. We expect that the proposed networks efficiently realize context-dependent in-network processing. By introducing recent findings of neuroscience about dynamics of the brain, we argue validity and clues for implementation of the proposal.
In this work we propose a two-party anonymous authenticated key exchange protocol that provides a communication binding property. The proposed protocol makes use of a compact structure to ensure key exchange and anonymity by adopting an anonymous implicit proof on the possession of a credential. We formally prove that the proposed protocol achieves anonymity, AKE-security, and a communication binding property. The protocol yields short communication messages and runs in two rounds. We show that our protocol is efficient via a comparison analysis with best-known anonymous authenticated key exchange protocols.
This paper proposes a disaster recovery method for transport networks. In a scenario of recovery from a disaster, a network is repaired through multiple restoration stages because repair resources are limited. In a practical case, a network should provide the reachability of important traffic in transient stages, even as service interruption risks and/or operational overheads caused by transport paths switching are suppressed. Then, we define the multi-objective optimization problem: maximizing the traffic recovery ratio and minimizing the number of switched transport paths at each stage. We formulate our problem as linear programming, and show that it yields pareto-optimal solutions of traffic recovery versus the number of switched paths. We also propose a heuristic algorithm for applying to networks consisting of a few hundred nodes, and show that it can produce sub-optimal solutions that differ only slightly from optimal solutions.
The MapReduce job scheduler implemented in Hadoop is a mechanism to decide which job is allowed to use idle resources in Hadoop. In terms of the mean job response time, the performance of the job scheduler strongly depends on the job arrival pattern, which includes job size (i.e., the amount of required resources) and their arrival order. Because existing schedulers do not utilize information about job sizes, however, those schedulers suffer severe performance degradation with some arrival patterns. In this paper, we propose a scheduler that estimates and utilizes remaining job sizes, in order to achieve good performance regardless of job arrival patterns. Through simulation experiments, we confirm that for various arrival patterns, the proposed scheduler achieves better performance than the existing schedulers.
Cloud computing is becoming increasingly popular. A large number of data are outsourced to the cloud by data owners motivated to access the large-scale computing resources and economic savings. To protect data privacy, the sensitive data should be encrypted by the data owner before outsourcing, which makes the traditional and efficient plaintext keyword search technique useless. So how to design an efficient, in the two aspects of accuracy and efficiency, searchable encryption scheme over encrypted cloud data is a very challenging task. In this paper, for the first time, we propose a practical, efficient, and flexible searchable encryption scheme which supports both multi-keyword ranked search and parallel search. To support multi-keyword search and result relevance ranking, we adopt Vector Space Model (VSM) to build the searchable index to achieve accurate search results. To improve search efficiency, we design a tree-based index structure which supports parallel search to take advantage of the powerful computing capacity and resources of the cloud server. With our designed parallel search algorithm, the search efficiency is well improved. We propose two secure searchable encryption schemes to meet different privacy requirements in two threat models. Extensive experiments on the real-world dataset validate our analysis and show that our proposed solution is very efficient and effective in supporting multi-keyword ranked parallel searches.
Support of incoming traffic differentiation and Quality of Service (QoS) assurance is very important for the development of high performance packet switches, capable of separating traffic flows. In our previous paper, we proposed the implementation of two buffers at each crosspoint of a crossbar fabric that leads to the Dual Crosspoint Queued (DCQ) switch. Inside DCQ switch, one buffer is used to store the real-time traffic and the other for the non-real-time traffic. We also showed that the static priority algorithms can provide the QoS only for the real-time traffic due to their greedy nature that gives the absolute priority to that type of traffic. In order to overcome this problem, in our paper we propose the DCQ switch with the Largest Weighted Occupancy First scheduling algorithm that provides the desired QoS support for both traffic flows. Detailed analysis of the simulation results confirms the validity of proposed solution.
A threshold secret sharing scheme protects content by dividing it into many pieces and distributing them among different servers. This scheme can also be utilized for the reliable delivery of important content. Thanks to this scheme, the receiver can still reconstruct the original content even if several pieces are lost during delivery due to a multiple-link failure. Nevertheless, the receiver cannot reconstruct the original content unless it receives pieces more than or equal to the threshold. This paper aims to obtain reliable delivery routes for the pieces, as this will minimize the probability that the receiver cannot reconstruct the original content. Although such a route optimization problem can be formulated using an integer linear programming (ILP) model, computation of globally optimum delivery routes based on the ILP model requires large amounts of computational resources. Thus, this paper proposes a lightweight method for computing suboptimum delivery routes. The proposed greedy method computes each of the delivery routes successively by using the conventional shortest route algorithm repeatedly. The link distances are adjusted iteratively on the basis of the given probability of failure on each link and they are utilized for the calculation of each shortest route. The results of a performance evaluation show that the proposed method can compute sub-optimum delivery routes efficiently thanks to the precise adjustment of the link distances, even in backbone networks on a real-world scale.
The potential of infrastructureless vehicular ad hoc networks (VANETs) for providing multihop applications is quite significant. Although the Epidemic Routing protocol performs well in highly mobile and frequently disconnected VANETs with low vehicle densities or light packet traffic loads, its performance degrades greatly in environments of high vehicle density together with heavy packet traffic loads that create serious bandwidth contention and frequent collisions. We propose a new epidemic routing protocol in urban environments called Greedy Zone Epidemic Routing (GZER), in which the neighbors of a vehicle are divided into different zones according to their physical locations. Each vehicle maintains a summary vector (SV) of packets buffered locally and zone summary vectors (ZSVs) of all packets buffered in each zone. Whether the infection will be transmitted in each zone is decided by the difference between SV and ZSV. Simulation results show that the proposed GZER protocol outperforms the existing solutions significantly, especially in the environments of high vehicle densities together with heavy packet traffic loads.
In this paper, we propose a novel energy-efficient sensor device management scheme called sensor device personalization (SDP) for the Internet of things (IoT) and wireless sensor networks (WSNs) based on the IEEE 802.15.4 unslotted carrier sense multiple access with collision avoidance (CSMA/CA). In the IoT and WSNs with the star topology, a coordinator device (CD), user devices (UDs), and sensor devices (SDs) compose a network, and the UDs such as smart phones and tablet PCs manage the SDs, which consist of various sensors and communication modules, e.g., smart fridge, robot cleaner, heating and cooling system, and so on, through the CD. Thus, the CD consumes a lot of energy to relay packets between the UDs and the SDs and also has a longer packet transmission delay. Therefore, in order to reduce the energy consumption and packet transmission delay, in the proposed SDP scheme, the UDs obtain a list of SD profiles (including SDs' address information) that the UDs want to manage from the CD, and then the UDs and the SDs directly exchange control messages using the addresses of the SDs. Through analytical models, we show that the proposed SDP scheme outperforms the conventional scheme in terms of normalized throughput, packet transmission delay, packet loss probability, and total energy consumption.
Recently, a malicious user attacks a web browser through a malicious page that exploits the vulnerability of the browser and that executes malicious code. To prevent this attack, some methods have been devised such as DEP (Data Execution Prevention) that prevents data in stack frame or heap region from being executed. However, to evade these defense techniques, return-oriented programming (ROP) is introduced. ROP executes arbitrary code indirectly using gadget, which is group of instructions including ret instruction in a module that doesn't apply ASLR (Address Space Layout Randomization). In this paper, we propose a static approach to detect ROP payload in a network irrespective of the environment of the system under attack. Most studies have tried to detect ROP attacks using dynamic analysis, because ROP has various addresses of gadgets according to loaded modules. These methods have a limitation that must consider the environment of system to operate ROP, such as the version of OS and modules including gadgets. To overcome this limitation, our method detects ROP payload using static analysis without preliminary knowledge about the environment. We extract five characteristics of ROP and then propose a novel algorithm, STROP, to detect ROP in payload without execution. Our idea is as follows: STROP makes stack frame using input payload statically. It extracts addresses suspected as indicating gadgets and makes groups using the addresses. And then, STROP determine whether the payload includes ROP based on static characteristics. We implement a prototype using snort (network-based intrusion system) and evaluate it. Experiments show that our technique can detect ROP payload with a low number of false alarms. False positive (FP) is 1.3% for 2,239 benign files and 0.05-0.51% for 1GB packet dump file. Among 68 ROP payloads, STROP detects 51 payloads. This research can be applied to existing systems that collect malicious codes, such as Honeypot.
Device-to-device (D2D) multicast communication is a useful way to improve the communication efficiency of local services. This study considers a scenario of D2D multicast communication in a single frequency network (SFN) system and investigates the frequency resource allocation problem. Firstly, we propose that D2D user equipments (DUEs) do not share frequency with cellular user equipments (CUEs) in the same SFN, but reuse frequency with CUEs in other SFNs, by which the interference between D2D and cellular communications can be avoided. Then, under the principle that two nearest D2D multicast groups cannot reuse the same frequency, the study develops a distance-based fair frequency resource allocation (DFRA) algorithm. The DFRA algorithm ensures control of the interference within a reasonable range and fairly allocate the available frequency resources to the D2D multicast groups. Numerical simulation results show that the proposed resource allocation algorithm is effective in improving the data rate and reducing the outage probability for D2D communications.
An optimal design method of linear processors intended for a multi-input multi-output (MIMO) full-duplex (FD) amplify-and-forward (AF) relay network is presented under the condition of spatial-domain self-interference nulling. This method is designed to suit the availability of channel state information (CSI). If full CSI of source station (SS)-relay station (RS), RS-RS (self-interference channel), and RS-destination station (DS) links are available, the instantaneous end-to-end capacity is maximized. Otherwise, if CSI of the RS-DS link is either partially available (only covariance is known), or not available, while CSI of the other links is known, then the ergodic end-to-end capacity is maximized. Performance of the proposed FD-AF relay system is demonstrated through computer simulations, especially under various correlation conditions of the RS-DS link.