nils-herrmann/clean_scirepeval huggingface dataset:
Design and Performance Evaluation of Overhearing-Aided Data Caching in Wireless Ad Hoc Networks

Wireless ad hoc network is a promising networking technology to provide users with Internet access anywhere anytime. To cope with resource constraints of wireless ad hoc networks, data caching is widely used to efficiently reduce data access cost. In this paper, we propose an efficient data caching algorithm which makes use of the overhearing property of wireless communication to improve caching performance. Due to the broadcast nature of wireless links, a packet can be overheard by a node within the transmission range of the transmitter, even if the node is not the intended target. Our proposed algorithm explores the overheard information, including data request and data reply, to optimize cache placement and cache discovery. To the best of our knowledge, this is the first work that considers the overhearing property of wireless communications in data caching. The simulation results show that, compared with one representative algorithm and a naive overhearing algorithm, our proposed algorithm can significantly reduce both message cost and access delay.
TED: Efficient type-based composite event detection for wireless sensor network

Although there are several works on providing event-based services in pervasive environment or WSN, most of them have not considered composite event detection in an energy-efficient fashion. Composite events consist of multiple primitive events with temporal and spatial relations and are much more difficult to manage. Because of the resource constraints in WSN, existing event detection algorithms may not be suitable for WSN when energy efficiency is considered. In this paper, we propose TED (Type-based composite Event Detection), a distributed composite event detection algorithm. The essential idea of TED is type-based event fusion, where some sensor nodes are selected as fusion points. Then lower-level events will be fused on these fusion points for detection of higher-level composite events. Each composite event type is assigned to certain fusion point for detection so that the composite events may be detected in-network instead of at the sink. Event fusion with minimum energy cost is an NP-complete problem. We propose a distributed randomized algorithm to solve the problem. We analyze the energy efficiency of TED to show both its effectiveness and efficiency. By carrying out both simulation and real world experiments on TED, we show that TED can reduce the energy cost by 10–20% in event-based WSN applications compared with naïve event detection mechanism where the event relations are not considered.
Exploiting Real-Time Traffic Light Scheduling with Taxi Traces

Traffic lights in urban area can significantly influence the efficiency and effectiveness of transportation. The real-time scheduling information of traffic lights is fundamentally important for many intelligent transportation applications, such as shortest-time navigation and green driving advisory. However, existing traffic light scheduling identification systems either entail dedicated infrastructures or depend on specialized traffic traces, which hinders the popularity and real world deployment. Differently, we propose to identify real-time traffic light scheduling by analyzing taxi traces that are widely accessible from taxi companies. The key idea is to exploit the periodicity in traffic patterns, which is directly affected by traffic lights. We also develop advanced algorithms to identify red/green lights duration and signal change time. We evaluate our solution using over one billion taxi records from Shenzhen, China. The evaluation results validate the effectiveness of our system.
Acoustic measurement of tidal bores in the Qiantang River, China

The repeat reciprocal sound transmission experiments for measurement of tidal bores were successful carried out in the upstream region of the Qiantang River in Hangzhou city, about 90 km from the mouth of the Hangzhou Bay during September 2009 to February 2010. Two Coastal Acoustic Tomography (CAT) Systems were set up at a distance of 3050 m for making a reciprocal sound transmission between the both banks of the Qiantang River. During the sound transmission experiments, the acoustic Doppler profiler and water level measurements were also performed. The range-averaged current velocities estimated from the acoustic data changed from 0.82 m/s to −1.18 m/s, and the water level changed from 0.18 m to 1.34 m, within a short period of about 20 minutes during the arrival of tidal bores. The mean propagation speeds of the tidal bores between two stations changed in the range of 5.76 m/s and 6.72 m/s with a mean of 6.33 m/s. The surge Froude numbers were estimated to be 0.82–1.13, which indicated the observed tidal bores were undular bores.
Biased constrain hybrid Kalman filter for wireless indoor localization

Many exist localization algorithms are unbiased estimators. However, the estimation performance presents biased feature in the real location systems. On the other hand, many biased location estimators show advantages that unbiased estimators can not achieve, e.g., robust to the noise, more accurate estimation and low complexity. In this paper, we propose a biased localization estimator and a hybrid Kalman filtering algorithm. The proposed algorithm is robust to the complicated environment with high accuracy. Both theoretical analysis and experimental evaluation indicate that the proposed algorithm outperform the unbiased optimal estimation methods.
Overhearing-Aided Data Caching in Wireless Ad Hoc Networks

The wireless ad hoc network is a promising networking technology to provide users with various network services anywhere anytime. To cope with resource constraints of wireless ad hoc networks, data caching is widely used to efficiently reduce data access cost. In this paper, we propose an efficient data caching algorithm for wireless ad hoc networks which makes use of the overhearing property of wireless communication to improve caching performance. Due to the broadcast nature of wireless links, a packet can be overheard by a node within the transmission range of the transmitter, even if the node is not the intended target. Our proposed algorithm explores the overheard information, including data request and data reply, to optimize cache placement and cache discovery. To the best of our knowledge, this is the first data caching algorithm that considers the overhearing property of wireless communications for performance improvement. The simulation results show that, compared with existing work, our proposed algorithm can significantly reduce both message cost and access delay.
Raccoon: A Novel Network I/O Allocation Framework for Workload-Aware VM Scheduling in Virtual Environments

We present a network I/O allocation framework, called Raccoon, for workload-aware VM scheduling algorithm to facilitate hybrid I/O workloads in virtual environments. Raccoon combines the strengths of paravirtual I/O and SR-IOV techniques to not only minimize the network latency, but also optimize the bandwidth utilization for workload-aware VM scheduling. In Raccoon, a limited number of VFs in SR-IOV are granted to I/O-intensive VMs while the paravirtual Network Interface Cards (vNICs) are allocated to other non-I/O-intensive VMs as the default resources. With this design, Raccoonprovides latency reduction and bandwidth guarantee under the premise that I/O-intensive VMs will always be granted the VFs to facilitate their I/O operations. The types of workloads in each VM are identified at runtime by modified XenMon. By leveraging the ACPI Hotplug technique, Raccoon can adaptively plugin and plugout the SR-IOV VFs upon the changes of VM requirements so that an efficient I/O workload-aware VM scheduling algorithm can be implemented based on the bonding driver technique. The experimental results reveal that Raccoon can combine the benefits of para-virtual I/O and SR-IOV techniques to improve the overall performance of virtualized platforms with VMs that have diverse I/O workloads.
When Taxi Meets Bus: Night Bus Stop Planning over Large-Scale Traffic Data

With more and more citizens traveling for life or work at night, there is a big gap between the demands and supplies for public transportation service in China. In this paper, we address the problem of night-bus stop planning by investigating the characteristics of taxi GPS trajectories and transactions, rather than leveraging subjective and costly surveys about citizen mobility patterns. There are two stages in our method. In the first stage, we extract the Pick-up and Drop-off Records (PDRs) from the taxi GPS trajectories and transactions for capturing citizens travel patterns at night. In the second stage, we propose DC-DBSCAN, an improved DBSCAN clustering algorithm by considering the Distance Constraint, to detect hot locations as candidate night-bus stops from the PDRs dataset. We take the service range of a bus stop into consideration, and optimize the candidates by considering the cost and convenience factors. Finally, our experiments demonstrate that our method is valid and with better performance than that of K-means.
Characterizing On-Bus WiFi Passenger Behaviors by Approximate Search and Cluster Analysis

On-bus WiFi emerges as a promising market in recent years. It is an interesting problem to investigate the characteristics of bus passengers by the spatial-temporal data collected by smart WiFi devices. In this paper, we analyze passenger behavior logs, including WiFi connection, WiFi disconnection, web authorization, data traffic, etc. We aggregate these passengers' activities into online events by finding out the relations among these activities. Then we cluster Origin-Destination pairs of all the trips in a month of each passenger to find passenger's interested points and overall distribution of passengers' interested points. Our results show that there is only 8.33% of on-bus WiFi passengers to explore the web. The average time used on on-bus WiFi only last 6 minutes in one connection, but total time which a passenger spends on on-bus WiFi is about 25 minutes a day. On-bus WiFi passengers' average data traffic of the network is periodical. It is growing slowly until Sunday while dropping down on Monday, which indicates passengers prefer to use on-bus WiFi more frequently at weekends. 44.7% of the passengers are active in only one place and 39% of the passengers are active in two places.
Road condition monitoring using on-board Three-axis Accelerometer and GPS Sensor

A study by US Federal Highway Administration has shown that road condition is an essential factor of highway quality and smooth roads will lead to more comfortable driving experience and less municipal investment. International Roughness Index (IRI) has been widely used to measure pavement smoothness because it can provide a consistent rating for different measurement tools. However, existing measuring tools based on IRI are usually very expensive. In this paper, we present a low-cost vehicle-based solution, Road Condition Monitoring with Three-axis Accelerometers and GPS Sensors (RCM-TAGPS), by using a cheap three-axis accelerometer and a GPS sensor embedded in a vehicle to monitor the road condition. We analyze the Power Spectral Density (PSD) of pavement roughness, estimate IRI, and classify the pavement roughness level into four levels according to a Chinese industry standard. Experimental results show that RCM-TAGPS can evaluate pavement roughness level correctly, even under some interference like potholes, manholes and decelerating belts, and the total cost of RCM-TAGPS in each vehicle is no more than 50 dollars, which is about 1/4400 to 1/160 of the existing system used in civil engineering and municipal engineering.
Free view generation in Ray-Space via the radon transform

Ray-Space interpolation is one of the key technologies to generate arbitrary viewpoint images so as to implement Ray-Space based FTV (Free Viewpoint Television) system. Since Ray-Space data is composed of straight lines with different slopes, how to find the direction of the lines is the problem to be solved in Ray-Space interpolation. In this paper, a radon transform based free view generation algorithm in Ray-Space is proposed. First, feature points of epipolar plane image (EPI) are extracted to form a feature EPI (FEPI). Then, Radon Transform is applied to each FEPI to find the possible interpolation direction. Finally, the optimal interpolation direction is determined by an improved block-based matching algorithm. Experimental results show that the virtual view point image generated by the proposed algorithm has much higher quality than that generated by the traditional pixel matching based interpolation (PMI) and block matching based interpolation (BMI).
Reciprocal sound transmission experiments for current measurement in a tidal river

Six repeat reciprocal sound transmission experiments for current measurement were successful carried out in the upstream region of the Qiantang River in Hangzhou city, about 90 km from the mouth of Hangzhou Bay during April to December 2009. Two Coastal Acoustic Tomography (CAT) Systems were set up at a distance of 3050 m for making a reciprocal transmission between the both banks of the Qiantang River. During the sound transmission experiments, thirty-two repeat shipboard Acoustic Doppler Profiler (ADP) surveys were also performed along the sound transmission line to get a comparison with the reciprocal sound transmission data. Range-averaged current velocities, determined from the travel time differences along the transmission line were in good agreement with those from the ADP, producing a root-mean-square difference of 0.03 m/s. The travel time differences data were well correlated with the river discharge estimated from the ADP data. The time series of river discharge during the experimental period was estimated through an empirical formula, relating to both the parameters. The variations of river discharge caused by tidal bores were well captured. The river discharge changed in the range of −7626 m3/s and 5096 m3/s, with a mean of 1246 m3/s. The above results suggest that the CAT is a powerful instrument for measuring continuously the river discharge in such tidal rivers with quite heavy shipping traffic.
A coastal acoustic tomography experiment in the Qiongzhou Strait

An experiment for synchronous mapping of current velocity (V CAT ) and temperature (T CAT ) distributions was successfully carried out using four coastal acoustic tomography (CAT) systems, from March 17 to April 1, 2013, in the Qiongzhou Strait, China. During the CAT observation period, shipboard acoustic Doppler current profiler (ADCP) surveys were also performed to observe the current velocity (V ADCP ) and temperature (T ADCP ) for the purpose of comparison with the CAT data. Position offsets, including a long-time averaged offset and a short-period fluctuation offset, were corrected for temperature estimation. The horizontal distribution of V CAT and T CAT was reconstructed by an inverse technique using the CAT data. V CAT was nearly parallel to the strait while T CAT was higher in the southern area than in the northern area. During the whole observation period, T CAT gradually increased by approximately 2°C, indicating seasonal warming. V CAT was in good agreement with V ADCP , while the root-mean-square differences (RMSDs) between V CAT and V ADCP for eastward and northward velocities were 0.10 m/s and 0.08 m/s, respectively. T CAT was in approximate agreement with T ADCP , with an RMSD of 0.30°C.
MCast: High-Quality Linear Video Transmission With Time and Frequency Diversities

Uncoded linear video transmission has recently attracted people’s attention due to its capacity to provide robust and scalable transmission. However, in reality, with the fluctuation of the wireless channels, the received quality may not be good enough. In such a case, the data may need to be transmitted multiple times to exploit both the time and frequency diversities to improve the received quality. Such a problem has never been investigated in the literature of uncoded video transmission. To resolve the problem, in this paper, we propose a framework, named MCast, to utilize the time and frequency diversities to achieve high-quality linear video transmission. We study how to optimally allocate the power and assign the channels at each time slot to the source data such that the overall performance is maximized. Specifically, we first derive a closed-form optimal power allocation solution for any given channel assignment. With the optimal power allocation, we then propose a suboptimal channel assignment scheme, where we sort the channels with their gains and assign the channels one-by-one to the corresponding block that can reduce the most reconstruction error. Finally, we compare the MCast system with four other systems that are based on Softcast and Parcast, and simulation results show that the MCast system can achieve better performance in terms of both the PSNR performance and visual quality.
Modeling Hierarchical Gossiping in Reliable Multicast Protocols

Although much work has been done on the design of gossip-based protocols, how to model and analyze the performance of such protocols is still a challenging task. The performance of a gossip-based protocol is significantly affected by its key parameters, e.g. the fanout of nodes. Determining the setting of such parameters through mathematical models is a key issue in gossip-based reliable multicast protocols. In this paper, we propose a generalized hierarchical gossiping algorithm and develop a mathematical model based on generalized random graphs to evaluate the reliability of hierarchical gossiping. Using our mathematical model, we investigate the impact of the parameters, namely the fanout distributions at the two levels of hierarchy on the reliability of hierarchical gossiping. We also give the critical condition for guaranteeing the gossiping messages to be propagated from local subgroups to the whole group. Simulations have been carried out and the results show that our analytical model is effective and accurate. The results obtained can be used to guide the design of hierarchical gossip-based protocols.
Web Access Patterns Enhancing Data Access Performance of Cooperative Caching in IMANETs

In an IMANET, mobile users access both text and media web contents on the Internet through gateway nodes, with web access patterns, i.e., the Zipf-like distribution or the Stretched Exponential distribution. To reduce data access delay from the Internet, we consider the cache placement problem in cooperative caching, which is that each mobile node selects a subset of web contents to cache cooperatively in its limited cache so that total access cost is minimized. It has been proved NP-hard. We propose a solution named Adaptive Allocation Cooperative Caching (AACC), which adaptively divides the cache space of each node into two components: altruistic and selfish, according to detected data access patterns. AACC aims to find the optimal cache allocation solution to allocate appropriate cache spaces for two components in order to minimize total access cost. Given the Zipf-like access distribution, we find a near-optimal allocation solution to the cache placement problem. Simulation results show that AACC achieves much better performance than the existing best cooperative caching strategy in IMANETs in terms of average query delay, caching overheads, and query success ratio. In particular, AACC reduces caching overheads by 40% in average.
Load distribution for multi-way streams joins using cluster

As the development of sensor, network, etc., more and more data are supplied in the form of streams. In most cases, it is essential to join multi-way streams in order to get potential information in real time. Unfortunately, as the volume of data increasing, the capability of single node model could not catch up with the application requirements. Executing the join process in a shared-nothing cluster is one of practical solutions. How to routing the streams to improve the throughput and guarantee the load balance in the cluster is an important problem. In this paper, we present a method named Time Segment policy to routing data streams in cluster. Our method can guarantee (1) the related tuples are routed to the same nodes; (2) load balance among all computing nodes under the promise of (1). The experiments show that our method can distribute streams effectively in the “master-worker architecture” cluster model.
On Modeling Fault Tolerance of Gossip-Based Reliable Multicast Protocols

Gossiping has been widely used for disseminating data in large scale networks. Existing works have mainly focused on the design of gossip-based protocols but few have been reported on developing models for analyzing the fault tolerance property of these protocols. In this paper, we propose a general gossiping algorithm and develop a mathematical model based on generalized random graphs for evaluating the reliability of gossiping, i.e., to what extent gossip-based protocols can tolerate node failures, yet guarantee the specified message delivery. We analytically derive the maximum ratio of failed nodes that can be tolerated without reducing the required degree of reliability. We also investigate the impact of the parameters, namely the fanout distribution and the non failed member ratio, on the protocol reliability. Simulations have been carried out to validate the effectiveness of our analytic model in terms of the reliability of gossiping and the success of gossiping. The results obtained can be used to guide the design of fault tolerant gossip-based protocols.
Data Caching in Next Generation Mobile Cloud Services, Online vs. Off-Line

In this paper we consider the data caching problem in next generation data services in the cloud, which is characterized by using monetary cost and access trajectory information to control cache replacements, instead of exploiting capacityoriented strategies as in traditional research. In particular, given a stream of requests to a shared data item with respect to a homogeneous cost model, we first propose a fast off-line algorithm using dynamic programming techniques. The proposed algorithm can generate optimal schedule within O(mn) timespace complexity to cache, migrate as well as replicate the shared data item to serve an n-length request sequence with minimum cost in a fully connected m-node network, substantially improving the previous results. Additionally, we also study this problem in its online form, and present a 3-competitive online algorithm by leveraging a speculative caching idea. The algorithm can serve an online request in constant time, and is space efficient in O(m) as well, rendering it to be more practical in reality. Our research complements the shortage of similar research in literature on this problem.
A Fusion Financial Prediction Strategy Based on RNN and Representative Pattern Discovery

To predicate the future with high accuracy is a holy grail in financial market. However, the volatility of chaotic financial market challenges new technologies from computer science to economic science all the time. Recently, Recurrent Neural Network (RNN) plays a new role in financial market prediction. However, results from RNN are restricted by sample size of training datasets, and show predication accuracy can hardly be guaranteed in a long term. On the other hand, Representative Pattern Discovery (RPD) is an effective way in long-term prediction while it is ineffective in short-term prediction. In this paper, we define a representative pattern for time series, and propose a fusion financial prediction strategy based on RNN and RPD. We take the advantages of both RNN and RPD, in the way that the proposed strategy is stateful to keep the short-term trend and it rectifies the predication by a time-dependent incremental factor in a long-term way. Compared with RNN and pattern discovery respectively, our experimental results demonstrate that our proposed strategy performs much better than that of others. It can increase the prediction accuracy by 6% on the basis of RNN at most, but at a cost of higher Mean Squared Error.