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A graph learning and augmentation (GLA) technique is proposed herein to solve the received signal power interpolation problem, which is important for preemptive resource allocation in location-aware communications. A graph parameterization results in the proposed GLA interpolator having superior mean-squared error performance and lower computational complexity than the traditional Gaussian process method. Simulation results and analytical complexity analysis are used to prove the efficacy of the GLA interpolator.

Digital twins (DT) and cyber-physical systems (CPS) are two independently developed yet comparable supporting concepts for smart manufacturing, or “Industry 4.0” in a more popular term. Both of their working principle are based on a close interaction between a physical object and its digital counterpart. Such similarity arouses our curiosity on the association between them. The purpose of this study is to highlight the associations between DT and CPS. With the help of co-word analysis, we find that the two terms seldom co-appear in the literature. Nevertheless, they link to a similar set of enabling-technology terms although with different weighting on these terms, implying that they are established on a similar set of technologies. We suggest that topics within a field being developed independent of each other in the early stage is a common phenomenon. The two currently separate literature streams are likely to merge into one in the foreseeable future. We also discuss the future prospect of DT. Other than manufacturing, DT has great potential in applications such as health care and e-commerce.

Modern convolutional neural network (CNN) models offer significant performance improvement over previous methods, but suffer from high computational complexity and are not able to adapt to different run-time needs. To solve above problem, this paper proposes an inference-stage pruning method that offers multiple operation points in a single model, which can provide computational power-accuracy modulation during run time. This method can perform on shallow CNN models as well as very deep networks such as Resnet101. Experimental results show that up to 50% savings in the FLOP are available by trading away less than 10% of the top-1 accuracy.

Smart-home is becoming increasingly popular in recent years, and it introduces a new content retrieval paradigm - delay-insensitive downloading. In this new paradigm, users do not require the content retrieval task to finish as soon as possible, but only set a deadline for it. We study the role of this paradigm in the traffic engineering of a chunk-based cloud storage service. We propose that it could help to reduce the high intra-datacenter traffic at peak resulting from the chunk-based architecture, by delaying users' content requests when necessary. Because of the introduction of the new paradigm, we consider the co- existence of three applications (downloading, streaming, delay-insensitive downloading) in the service, and try to understand the best way to delay users' requests. We conduct an incentive-based study for the evaluation of schemes of delaying users' requests: the service provider pays incentives to users to promote this new paradigm. The incentive, as well as users' application-specific QoE on the service, is modelled, and a framework for the evaluation is presented. We then apply our framework to study several proposed delay schemes and present both quantitative and qualitative results.

In recent years emerging ride-on-demand services (eg., Uber or Didi) are penetrating into the market of traditional taxi service. In these new services mobile devices are a key enabler: they serve as the intermediary between passengers/drivers and the service provider, tracking the locations and behavior of both passengers and drivers. On the other hand, the use of mobile devices also help us to capture huge amount of data for analysis. Through collaboration with a leading service provider in China, we collect vast amount of accurate data and analyze, in this paper, passenger reaction to dynamic prices in such a service. We consider the analysis as an important step towards making the service more efficient and more attractive to the passengers. We present the patterns of passengers' reaction, and discuss if it is useful to estimate the trip fare for multiple times in order to get a lower price. Our findings pave the way for future study on system optimization and policy considerations.

Emerging ride-on-demand services (e.g., Uber or Uber-like) are vying to penetrate into the market of traditional taxi service, and they are ubiquitous in the nature, by using smart mobile devices like on-car GPS and mobile phone. These ubiquitous services are also beneficial for the environment by increasing the utilization of cars and improving travel efficiency. Through collaboration with a leading service provider in China, we are able to collect vast amount of accurate data and analyze the nature of the demand and dynamic pricing mechanisms that match the supply with demand. We consider the analysis as an important step towards making the ubiquitous service more efficient and beneficial to the sustainability of future smart cities. We collect datasets of passengers' orders and payment information, and focus on the analysis of demand and dynamic pricing. In demand analysis, we discuss its general characteristics, passenger grouping and demand clustering; in dynamic pricing analysis, we discuss the pattern and determination of dynamic pricing multipliers. Our findings pave the way for future study on system optimization, dynamic pricing and policy considerations.

Skyline queries are commonly used for database systems and mostly based on the notion of pareto-dominance. In reality, however, a user may have her/his tolerance value for each individual attribute when comparing the records, rather than a strict comparison of the respective values. This work proposes the idea of tolerance-based dominance and the associated tolerance-based skyline query (TSQ), which allows the incorporation of user-specified tolerance tuple to relax the rigidness of the classic skyline. To this end, we present the definition of tolerance-based dominance relation and the TSQ accordingly. We then exploit the relationship between pareto-based dominance and tolerance-based dominance along with grid properties to present an efficient grid-based scheme for processing a TSQ with arbitrary tolerance tuple. Our proposed technique essentially converts the TSQ processing operation into simple pareto-based dominance checking over grid space. Extensive experiments have been conducted to evaluate the performance of the proposed method.

We study the problem of how to manage the high intra-datacenter traffic in a chunk-based public cloud storage service serving primarily smart home devices. The large volume of traffic is introduced by delivering very large content during busy hours in the cloud. Measurement of a commercial cloud service shows that the peak traffic volume (at its edge servers) overwhelms the network interface cards (NICs), resulting in serious congestion and packet losses. Since it can be expected the large content downloading requests in smart home environment could be delay-insensitive, we propose DST to keep the peak load under a specified upper bound, by delaying users' requests when necessary. By modelling DST as a queueing system, we derive the relation between the mean delay and the traffic upper bound. With trace-driven simulations, we evaluate the system performance and validate the analysis results. For the commercial cloud service we study, we show that it is possible to keep the traffic upper bound to about 80% of peak traffic rate by introducing a mean delay of around 48 minutes.

Cloud computing is becoming increasingly popular because it offers valuable benefits, such as on-demand services, resource pooling, rapid elasticity, measured service, etc. However, recent studies indicate that many enterprises still hesitate to adopt cloud services due to various security concerns and implementation obstacles. Particularly, there is little research devoted to exploring the influences on cloud migration execution. To bridge this gap, our study derived some comprehensive cloud migration execution influences by interview approach, based on the technology-organization-environment (TOE) theory. We posit that system configuration (the technological aspect, which consists of complexity and compatibility), organizational fit (the organizational aspect, which consists of member reflection and operation process), and external support (the environmental aspect, which affects the migration execution of cloud computing). The findings in this paper can help enterprises facilitate various cloud migration activities, achieve their goals of migrating applicable legacy applications to the cloud environment, and realize the benefits provided by the cloud computing paradigm.

Video popularity (measured by view count) over time is an essential reference for both online video providers and users. According to state-of-the-art works, video popularity is useful for system optimization, load generation, video caching, and video recommendation. Thus, deeper understanding of video popularity evolution is very helpful for improving video service quality and providers' operating efficiency. The core question to be explored in this paper is what key factors govern online video popularity evolution? Through collaboration with our industry partner, Tencent Video, we obtain historical data of video view counts over a period of time, and observe their patterns. We then propose a stochastic fluid model, named as EvoModel, which captures two processes giving rise to different evolution patterns of a given video: (a) the information spreading process and (b) the user reaction process. The driving forces for process (a) can be either via recommendation from the system directly, or word-of-mouth; the extent of the spread is governed by the intrinsic popularity of the video. The factor affecting the second process can be modeled by a user reaction rate. These processes together determine different video popularity evolution patterns. We validate our model by fitting the historical data obtained from a real-world system. Furthermore, we discuss the feasibility of estimating model parameters and predicting popularity. Refereed/Peer-reviewed

There is an increasing demand for mobile video streaming. In order to improve the QoE of mobile video streaming, it is important to understand the user interest, user behavior, and current QoE achieved in mobile video streaming systems. Past studies have compared mobile video streaming with that for non-mobile end systems (PCs). In this paper, we report the differences in user interest, user behavior and QoE for different mobile device types (phone versus tablets, and Android versus iOS). Our study is based on a large data set collected from a real world mobile video streaming service operated by our collaborator. Besides reporting the differences for these device types, we also report the extent the streamed video is wasted (not viewed). It shows that there is a lot of room for improving the service QoE and efficiency.

Welcome to COMSNETS 2015! The final technical program this year consists of 35 regular papers and 10 invited presentations. We received 163 paper submissions from authors in 11 countries.

We study the problem of managing high intradatacenter traffic in a chunk-based public cloud storage service. The high traffic volume is introduced by aggregating very large files from multiple chunk servers in a single edge server. We measure a commercial cloud storage service system, and observe that peak traffic volume overwhelms the network interface cards (NICs) significantly. In the scenario of delivering content based on Smart Home network, it can be expected the file downloading service could be delay-insensitive. Thus, we propose “Batching Smooth intra-datacenter Traffic” (BST) scheme to reduce the peak load to a specified upper bound by batching and delaying users' requests. We resort to a mathematical model to understand the necessity of batching strategy. To evaluate BST's effects, we implement trace-driven simulations with different scheduling policies. In the commercial cloud storage service system, we show that BST is capable of keeping the upper bound to approximately 75% of the original peak traffic by trading off an average delay of 8 minutes.

On-line analytical processing (OLAP) provides analysis of multi-dimensional data stored in a database and achieves great success in many applications such as sales, marketing, financial data analysis. OLAP operation is a dominant part of data analysis especially when addressing a large amount of data. With the emergence of the MapReduce paradigm and cloud technology, OLAP operation can be processed on big data that resides in scalable, distributed storage. However, current MapReduce implementations of OLAP operation processing have a major performance drawback caused by improper processing procedure. This is crucial when dimension or dependent attributes are large, which is a common case for most data warehouses hold nowadays. To solve this issue, this paper proposes a methodology to accelerate the performance of OLAP operation processing on big data. We have conducted the experiments on the basic algebra of OLAP operation with different data sizes to demonstrate the effectiveness of our system.

In this work, fast binary to binary-coded-decimal (BCD) code converters for decimal communications are proposed. By employing new recoding circuits, our proposed converters can perform fast and area-efficient conversions from binary inputs to decimal outputs to speed up decimal multiplications. Synthesis results show that our proposed converters can achieve over 60% delay with slight area increase compared with previous proposed method, respectively. Our proposed converters can be applied to decimal multiplications to boost the decimal communications.

This paper introduces a scheme of client device identification by clock skew in cloud environments. Clock skew of a remote device is considered an effective physical characteristic and is suitable for device identification purposes. As the temperature is always steady inside data center, we utilize this advantage to verify that two arithmetic relations hold with negligible measurement error between servers and develop a fault-tolerant multi-server skew measuring scheme. We have also implemented a lightweight application layer experimental platform to provide the device identification and account authentication service in an implicit manner based on the clock skew fingerprinting technique. Two experiments are conducted to confirm the correctness of skew arithmetic, and to examine the feasibility of client device identification by clock skew respectively. The experiment results show that the maximum error of additive inverse is only 0.01 ppm and the linearity of clock skew holds with error no more then 0.03 ppm while utilizing physical hardware. Furthermore, the capability of load balance and fault tolerance achieved by multiple time servers are also demonstrated in the experiments.

In this paper, we present a pedestrian tracking system by using image segmentation algorithm, which incorporated pedestrian shape prior into Random Walks segmentation [1] from a static image, and tracking people by Connected Component Labeling Algorithm. We improve the random walks segmentation algorithm by using prior shape information, which provides appropriate seeds for the pedestrian segmentation from the input image. By using the human shape prior information, we develop a fully automatic pedestrian image segmentation algorithm to detect pedestrians. Then we can find the region of interest (ROI) often performed on using a mapping-based detection approach by Connected Component Labeling Algorithm. After these previous steps, we provide a pedestrians tracking system.

Popular online Video-on-Demand (VoD) services all maintain a large catalog of videos for their users to access. The number of servers assigned to serve each video is directly related to the relative popularity of the video. The distribution of popularity at a given time is quite well understood. We study how the video popularity changes over its lifetime, for different types of videos. Our study is based on analyzing the video access levels over time, based on data provided by a large video service provider. Our main finding is, while there are variations, the glory days of a video typically pass by quickly and probability of replaying a video by the same user is low. The reason appears to be due to fairly regular number of users and view time per day for each user, and continuous arrival of new videos. We then discuss the implication of our findings for video replication and recommendation. Refereed/Peer-reviewed

Because malicious code gradually endangers people's life and verifying the harmlessness of a file usually costs many resources, the MapReduce System for Online File Scanning (MRSOFS) is proposed as a solution for combing the online file scanning service and a MapReduce system on cloud computing. In this paper, MRSOFS is introduced, implemented, and evaluated with experiments. In experiments, MRSOFS outperforms the standard case of using a single computer to scan a file for a specific signature in a signature database. MRSOFS is proved to get performance benefits by using a MapReduce system to scan a file for a specific signature, and can be utilized by an online file scanning service against malicious code.

A 2.4 GHz fully-integrated MISO transceiver consisting of two receivers and one transmitter is implemented in 0.18 mum CMOS technology. To alleviate the cost of external front-end components, the RF transmit/receive (T/R) switch and a power-efficient linear CMOS PA are fully integrated on-chip. It shows 3.5 dB low noise figures in the receivers respectively. Also, the transmitter delivers an average power of 15.6 dBm OFDM (64 QAM, 54 MBPS) signal with EVM of -28.7 dB. The MISO transceiver in full operation consumes 86 mA in each Rx chain and 255 mA in Tx mode from 3.3 V supply.

A big portion of Internet traffic nowadays is video. A good understanding of user behavior in online VoD systems can help us design, configure and manage video content distribution. With the help of a major video on demand (VoD) service provider, we conduct a detailed study of user behavior watching streamed videos over the Internet. We engineered the video player at the client side to collect user behavior reports for over 540 million sessions.In order to isolate the possible effect of session quality of experience (QoE) on user behavior, we focus on the sessions with perfect QoE, and leave out those sessions with QoE impairments (such as freezes).Our main finding is that users spend a lot of time browsing: viewing part of one video after another, and only occasionally (around 20% of the time) watching a video to its completion. We consider seek (jump to a new position of the video) as a special form of browsing - repeating partial viewing of the same video. Our analysis leads towards a user behavior model in which a user transitions through a random number of short views before a longer view, and repeats the process a random number of times. A purely abstract version of such user behavior model was proposed by Wu et al [1] as a closed queueing network formulation. Our study uncovers the parameters and distributions of such a stochastic behavior model based on observations in practice. Refereed/Peer-reviewed

Target detection is an important problem in wireless sensor networks where a number of sensors form a network to detect the presence or absence of a certain target or event. Data fusion is a potential method broadly used to improve detection performance when the sampling data are noisy. However, low detection probability cannot be avoided if detection decisions are made based on a collection of sampling data taken at just one particular moment. This paper adopts fusion-based sequential detection to guarantee the quality of detection results. A fusion center is used to collect local data from individual sensors periodically. A final detection decision is made only after the pre-defined constraints of false alarm and missing probability are satisfied. Rules for each sensor to make local decisions and for the fusion center to make global decisions are derived. Simulations are conducted to show the latency of making the final decisions based on the proposed fusion scheme.

In recent years, use of mobile robot acting as a data mule for collecting data in the wireless sensor network has become an important issue. This data collection problem of generating a path as short as possible for a data mule to gather all data from all of sensor nodes is known as a NP-hard problem named Traveling Salesman Problem with Neighborhoods (TSPN). We proposed a clustering-based genetic algorithm (CBGA) capable of further shortening the TSPN route provided by clustering with demonstrated effectiveness and reduced computational complexity. In this paper, we seek effective implementation of CBGA by extensive simulations. An improved clustering-based genetic algorithm is proposed, which consists of a waypoint selection method and a GA with an appropriate combination of modified sequential constructive crossover (MSCX) operator and a mutation operator based on local optimization heuristics of 2-opt developed for TSP. Extensive simulations are performed to illustrate the effectiveness and improved performance of CBGA with a more effective GA implementation composed of a combination of MSCX crossover operator and 2-opt for path planning of a data mule.

Peer-to-peer (P2P) media streaming systems have recently become a major type of application traffic. In these applications, an important issue has been the block scheduling problem, which determines how each peer exchanges the data blocks from others. Scalable streaming in P2P networks has recently been proposed to address the heterogeneity of the network environment. In this paper, we first define a priority function for each block according to the block's significance for video content. The block scheduling problem is then transformed to an optimization problem that maximizes the priority sum of the delivered video blocks. The simulation results show that the proposed algorithm offers excellent performance for P2P streaming service.

The spatial query has been one of the highly demanded services in mobile computing system recently. To protect users' location privacy, existing architecture provides a trustworthy anonymizer to blur users' location from the service provider. However, with mobile capability, users' location extends from one spot to a continuous traveling route. For such continuous spatial query, it raises much more challenges for an anonymizer to protect users' continuous privacy. This paper conducts research on ensuring users' location privacy under the network-constrained road network environments. We first argue that the concept of continuous location privacy should be transferred to users' path privacy, which are consecutive road segments that needs to be protected. A novel M-cut requirement is proposed to achieve the goal of user path privacy. Mobile users can customize their privacy level through M-cut requirement. Last, two methods of constructing the cloaked spatial region are provided in our research, namely Random Selection and Junction Sharing. These algorithms support path privacy and also take system computation and communication overhead into consideration.

This paper describes a systematic approach to the control design of voltage-sourced inverters (VSIs). The proposed method can unify voltage and current controllers into the system model based on the internal model principle. Not only the system dynamic can be characterized mathematically, but the control object can follow its reference input without incurring steady-state error. To further adapt the VSI controller over diverse operating points along with satisfactory performance, the control gains are scheduled corresponding to various loading conditions in order to achieve the desired time-domain specifications portrayed by closed-loop eigenvalues. The experimental results confirm the performance of the proposed strategy.

Keywords of academic papers are jargons shared within a research domain as well as a summary of the contents. However, the increasing popularity of the interdisciplinary research in academia in recent years opened a possibility that the choice of keywords would be no longer confined by the traditional domain boundaries. In order to investigate the evolutionary trends of keyword-sharing groups in academic publications, we applied three types of Social Network Analysis methods on the papers in the Computer Science domain collected from the Libra database. Particularly, papers published in 5 different time periods from 1970 to 2010 were targeted to show the change of keyword networks over time. The analysis results confirmed the increasing tendency in the keyword network to be absorbed by one big agglomerated cluster over time. As a result, the gatekeeper- or liaison keywords seldom exist in later times but coordinator keywords became majority.

Localization is an important process in deploying ad-hoc wireless sensor networks. Several localization algorithms have been developed. However, they do not achieve satisfactory performance on irregular networks. In this paper, we present a localization algorithm based on an idea of growing local maps. This paper presents an ongoing work on a localization mechanism applied to wireless sensor networks. This mechanism can cooperate with hexagon method and trilateration and dynamic robot node to estimate the coordinates of some unknown nodes. It can estimate the sensor nodes that have no GPS with some other nodes that have GPS devices. Thereafter, we can obtain more unknown nodes with some estimated nodes. It decreases the cost that localization tracking of wireless sensor devices.

We consider a peer-to-peer (P2P)-assisted video-on-demand (VoD) system where each peer can store a relatively small number of movies to offload the server when these movies are requested. User requests are stochastic based on some movie popularity distribution. The problem is how to replicate (or place) content at peer storage to minimize the server load. Several variations of this replication problem have been studied recently with somewhat different conclusions. In this paper, we first point out and explain that the main difference between these studies is in how they model the scheduling of peers to serve user requests, and show that these different scheduling assumptions will lead to different "optimal" replication strategies. We then propose a unifying request scheduling model, parameterized by the maximum number of peers that can be used to serve a single request. This scheduling is called Fair Sharing with Bounded Degree (FSBD). Based on this unifying model, we can compare the different replication strategies for different degree bounds and see how and why different replication strategies are favored depending on the degree. We also propose a simple (primarily) distributed replication algorithm and show that this algorithm is able to adapt itself to work well for different degrees in scheduling. Refereed/Peer-reviewed

Since todays smartphones are programmable and embed various sensors, these phones have the potential to change the way how healthcare is delivered. Fall detection is definitely one of the possibilities. Injuries due to falls are dangerous, especially for elderly people, diminishing the quality of life or even resulting in death. This study presents the implementation of a fall detection prototype for the Android-based platform. The proposed system has three components: sensing the accelerometer data from the mobile embedded sensors, learning the relationship between the fall behavior and the collected data, and alerting preconfigured contacts through message while detecting fall. We adopt different fall detection algorithms and conduct various experiments to evaluate performance. The results show that the proposed system can recognize the fall from human activities, such as sitting, walking and standing, with 72.22% sensitivity and 73.78% specificity. The experiment also investigates the impact of different locations where the phone attached. In addition, this study further analyzes the trade-off between sensitivity and specificity and discusses the additional power consumption of the devices.

Assigning roles to individual students can influence the group's knowledge construction (KC) process during online discussions. Twenty-one students were divided into two groups and assigned rotating roles for eight one-week asynchronous online discussions. The KC contributions of all 252 posts in the discussion were coded using a five phase scheme and statistical discourse analysis was applied to identify segments of discussion characterized by particular aspects of KC plus "pivotal posts"--those posts which initiated new segments of discussion. Finally, the influences of assigned student roles on pivotal posts and KC were modeled. The results indicate that most online discussions had a single pivotal post separating the discussion into two distinct segments: the first dominated by a lower KC phase, the second dominated by a higher KC phase. The pivotal posts that initiated later segments were often contributed mid-discussion by students playing one of two summarizing roles (Synthesizer and Wrapper).

Wireless broadcasting of scalable video coded medium grain scalable (SVC/MGS) bit streams requires unequal erasure protection (UEP) at the transport layer in order to ensure graceful degradation of playback video quality over a wide range of frame error rates. Modern wireless broadcasting systems even employ rate less fountain codes to aid the receivers in making inevitable trade¬offs among picture quality, channel throughput and playback latency. Designing a rate less UEP channel code fits for such an application posts distinct engineering challenges as the neces¬sary protection for SVC base and enhancement layers differ by orders of magnitude while their intradependent groups of pictures fluctuate notably in their sizes. In this paper, we present the design and implementation of a rate less UEP con¬volutional code that meets these demanding requirements. Use of this UEP channel code along with rate-distortion based network application layer unit extraction offer sufficient protection to SVC bit streams under different lossy conditions without the need to re-code the bit stream. We also investigated the differences in playback performance of SVC bit streams that were protected by rate less codes vs. con¬ventional Reed-Solomon codes. The comparison makes clear the advantages and the disadvantages of employing rate less codes in protecting wireless video broad-casting.

In this paper, we study the impact of the image quality (hence, the video streaming bit-rate) and the chunk-level impairment (hence the playback discontinuity) on the perceptual assessment of the viewing experience of P2P layered video streaming services. Through subjective QoE experiments, we have obtained several interesting findings and useful insights. The results clearly reveal the tradeoffs between streaming bitrate (B) and discontinuity (D) on the MOS under ceratin network condition. It is also showed that B-D tradeoff pattern is video content dependant. Based on these observations, a heuristic model is proposed to derive a group of MOS contours. This type of contours have practical usage and can help selecting suitable video streaming bit-rate (layer) for playback so as to maximize the perceptual QoE of the end users.

Electrocardiography (ECG) is a transthoracic interpretation of the electrical activity of the human's heart over time captured and is highly irregular, random, and variable from person to person. Recently, the literature has revealed that this kind of signal is, in fact, chaotic. Because of people's ECGs are extremely hard to be artificially duplicated, this paper intends to investigate the way of extracting the ECG signals' biometric features for the possibility of biometric recognition. The ECG signal is converted into the phase plane by using phase space reconstruction. Then, chaos extractor is applied to capture the representative indices of chaotic ECG signals i.e., correlation dimension and Lyapunov exponents spectrum. The root mean square, Lyapunov exponent and correlation dimension are used as the key variables in neural network training and utilized in the identification scheme.

Traditional Video-on-Demand (VoD) systems reply purely on servers to stream video content to clients, which does not scale. In recent years, Peer-to-peer assisted VoD (P2P VoD) has proven to be practical and effective [1]. In P2P VoD, each peer contributes some storage to store videos (or segments of videos) to help the video server. Assuming peers have sufficient bandwidth for the given video playback rate, a fundamental question is what is the relationship between the storage capacity (at each peer), the number of videos, the number of peers and the resultant off-loading of video server bandwidth. In this paper, we use a simple statistical model to derive this relationship. We propose and analyze a generic replication algorithm RLB which balances the service to all movies, for both deterministic and random demand models, and both homogeneous and heterogeneous peers (in upload bandwidth). We use simulation to validate our results, for sensitivity analysis and for comparisons with other popular replication algorithms. This study leads to several fundamental insights for design P2P VoD systems in practice. Refereed/Peer-reviewed

Wireless sensor network (WSN) technology is widely used in environment monitoring, health care, surveillance systems and unmanned space or planet exploration. This paper focuses on routing problems with data gathering by a mobile robot in a WSN, also referred to as a Traveling Salesman Problem with Neighborhoods (TSPN) or NP-hard problem. In this paper, we propose a clustering-based parallel genetic algorithm with migration (CBPGA), so that the mobile robot can gather all data from all sensors and the travel costs of the mobile robot clearly decrease. First, a clustering algorithm is used to effectively reduce the number of visited nodes, especially in situations with dense sensor distributions or large sensing radii. Next, the set of visited nodes is encoded as chromosomes by a chromosome generation algorithm (CGA), and the master-slave parallel genetic algorithm with migration is performed to more efficiently generate the near-optimal route. Lastly, a travel cost-reduction scheme is used to remove redundant travel costs. Simulation results confirm that the clustering-based parallel genetic algorithm with migration more efficiently generates a near-optimal route that reduces the travel costs of a mobile robot in robot routing problems with WSN.

The information of temporal correlations among network-wide data flows is crucial to a wide range of network management applications, such as root-cause analysis, threat monitoring, and traffic profiling. While several prior work had only studied the centralized and offline computation of flow correlations, we present DisTrack, a space-efficient network management mechanism for online tracking of network-wide temporal flow correlations. The major benefits of DisTrack include low space complexity, high processing speed, and ease of distributed deployment. This paper presents its randomized data structures, with theoretical analysis on the trade-off between space complexity and accuracy. We further provide extensive empirical evaluations on real network traces.

In this paper, we focus on the perceptual quality assessment of network streamed video via P2P networks. In these systems, the most noticeable impairments are not bit-level distortions, nor packet losses (as they get repaired), but video chunk or frame level delays. A key module in a video player is the video playback controller that decides whether to wait for delayed frames (hence freezing the playback), or try to playback by concealing the missing data. A good design should be based on how users react to different frame delay scenarios and playback decisions. We carefully designed a set of subjective tests to sample user reactions, and discuss how our results can be used in the design of playback controllers.

NAND flash memory has gained its popularity in a variety of applications as a storage medium due to its low power consumption, non-volatility, high performance, physical stability, and portability. In particular, Multi-Level Cell (MLC) flash memory, which provides a lower cost and higher density solution, has occupied the largest part of NAND flash-memory market share. However, MLC flash memory also introduces new challenges: (1) Pages in a block must be written sequentially. (2) Information to indicate a page being obsoleted cannot be recorded in its spare area. This paper designs an MLC Flash Translation Layer (MFTL) for flash-memory storage systems which takes new constraints of MLC flash memory and access behaviors of file system into consideration. A series of trace-driven simulations is conducted to evaluate the performance of the proposed scheme. Our experiment results show that the proposed MFTL outperforms other related works in terms of the number of extra page writes, the number of total block erasures, and the memory requirement for the management.

Routing failures are common on the Internet and routing protocols can not always react fast enough to recover from them, which usually causes packet delivery failures. To address the problem, fast reroute solutions have been proposed to guarantee reroute path availability and to avoid high packet loss after network failures. However, existing solutions are often specific to single type of routing protocol. It is hard to deploy these solutions together to protect Internet routing including intraand inter-domain routing because of their individual computational and storage complexity. Moreover, most of them can not provide effective protection for traffic over failed links, especially for the bi-directional traffic. In this paper, we propose a unified fast reroute solution for routing protection under network failures. Our solution leverages identifier based direct forwarding to guarantee the effectiveness of routing protection and supports incremental deployment. In particular, enhanced protection cycle (e-cycle) is proposed to construct rerouting paths and to provide node and link protection for both intraand inter-domain routing. We evaluate our solution by simulations, and the results show that the solution provides 100% failure coverage for all endto-end routing paths with approximately two extra Forwarding Information Base (FIB) entries.

Paris Metro Pricing (PMP) is a simple multi-class flat-rate pricing scheme already practiced by transport systems, specifically by the Paris Metro at one time. The name is coined after Andrew Odlyzko proposed it for the Internet as a simple way to provide differentiated services. Subsequently, there were several analytical studies of this promising idea. The central issue of these studies is whether PMP is viable, namely, whether it will produce more profit for the service provider, or whether it will achieve more social welfare. The previous studies considered similar models, but arrived at different conclusions. In this paper, we point out that the key is how the users react to the congestion externality of the underlying system. We derive sufficient conditions of congestion functions that can guarantee the viability of PMP, and provide the relevant physical meanings of these conditions.

Electrocardiogram (ECG) signals varying from person to person, it could possibly be applied as a tool for biometric recognition. This paper attempts to introduce an individual feature of ECG with a chaotic Henon map for cryptography. The encryption system utilizes a portable instrument (Heart Pal) to collect ECG signals from the encrypted person and applies an intelligent algorithm based on Chaos theory to generate initial keys for chaotic Henon map. High quality randomness of ECG signals results in a widely expanded key space which would be an ideal key generator for data encryption.

Data-driven P2P streaming systems can potentially provide good playback rate to a large number of viewers. One important design problem in such P2P systems is to determine the optimal chunk selection policy that provides high continuity playback under the server's upload capacity constraint. We present a general and unified mathematical framework to analyze a large class of chunk selection policies. The analytical framework is asymptotically exact when the number of viewers is large. More importantly, we provide some interesting observations on the optimal chunk selection policy: it is of shaped and becomesmore greedy as the upload capacity of the server increases. This insight helps content providers to deploy large scale streaming systems with a QoS-guarantee under a given cost constraint.

Though many Bluetooth ACL packet types have been presented to satisfy various requirements, only one fixed packet type is used during the whole transmission period in the Bluetooth products. Therefore, the Bluetooth system will not yield the maximum data throughput since it cannot adapt the transmit packets to the dynamic channel conditions. To overcome this difficulty, a simple mode/packet switching scheme based on Bluetooth ARQN statistics is proposed in this paper. The results of simulation experiments carried out over AWGN and Rayleigh fading channels indicate that the proposed scheme can effectively track channel variations to switch operation mode and transmit packet and thereby significantly increases Bluetooth data throughput.

This work presents a wide range pulse-width control loop circuit. The input control stage is designed with ratioless circuit without dissipating dc power. The differential signals of the charge pump increase the accuracy of output duty cycle. The characteristics of complementary signals accomplished by a connection of differential second-order low-pass filter between complementary charge pumps can effectively increase the stability and reduce the capacitor area. This work introduces a new approach in receiving wide-range input and output duty cycles at an extended bandwidth. Simulation verifies that the duty cycle range of input and output clock is from 1% to 99% with resolution of 1%.