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With the increasing pervasiveness of mobile devices such as smartphones, smart TVs, and wearables, smart sensing, transforming the physical world into digital information based on various sensing medias, has drawn researchers' great attention. Among different sensing medias, WiFi and acoustic signals stand out due to their ubiquity and zero hardware cost. Based on different basic principles, researchers have proposed different technologies for sensing applications with WiFi and acoustic signals covering human activity recognition, motion tracking, indoor localization, health monitoring, and the like. To enable readers to get a comprehensive understanding of ubiquitous wireless sensing, we conduct a survey of existing work to introduce their underlying principles, proposed technologies, and practical applications. Besides we also discuss some open issues of this research area. Our survey reals that as a promising research direction, WiFi and acoustic sensing technologies can bring about fancy applications, but still have limitations in hardware restriction, robustness, and applicability. [ABSTRACT FROM AUTHOR]

A tremendous amount of data has been generated by global financial markets everyday, and such time-series data needs to be analyzed in real time to explore its potential value. In recent years, we have witnessed the successful adoption of machine learning models on financial data, where the importance of accuracy and timeliness demands highly effective computing frameworks. However, traditional financial time-series data processing frameworks have shown performance degradation and adaptation issues, such as the outlier handling with stock suspension in Pandas and TA-Lib. In this paper, we propose HXPY, a high-performance data processing package with a C++/Python interface for financial time-series data. HXPY supports miscellaneous acceleration techniques such as the streaming algorithm, the vectorization instruction set, and memory optimization, together with various functions such as time window functions, group operations, down-sampling operations, cross-section operations, row-wise or column-wise operations, shape transformations, and alignment functions. The results of benchmark and incremental analysis demonstrate the superior performance of HXPY compared with its counterparts. From MiBs to GiBs data, HXPY significantly outperforms other in-memory dataframe computing rivals even up to hundreds of times. [ABSTRACT FROM AUTHOR]

It is of significant importance to provide network-wide Quality of Service (QoS) for a wide range of event detection applications in wireless sensor networks. This paper investigates the important problem of QoS provision to abnormal event detection. For event detection applications, there are two key performance metrics, i.e., detection probability and detection latency. This paper considers both metrics, aiming to provide statistical QoS for abnormal event detection. It is, however, a challenging issue because of stringent resource constraints of sensor nodes and unpredictable randomness of physical events. We propose a probabilistic approach to statistical QoS provision for event detection in sensor networks. We propose a distributed algorithm that iteratively determines the active probability of each sensor node. The probability is kept small for energy efficiency but sufficiently large to provide the required detection QoS. Our approach is flexible and can detection QoS customized by applications. Comprehensive simulation experiments have been conducted, which demonstrate that our approach is able to provide the required detection QoS for event detection and achieves considerably longer the system lifetime compared with other competing schemes. [ABSTRACT FROM AUTHOR]

Most existing connectivity-based localization algorithms require high node density which is unavailable in many large-scale sparse mobile networks. By analyzing large datasets of real user traces from Dartmouth and MIT, we observe that user mobility exhibits high spatiotemporal regularity and, more importantly, that user mobility is strongly correlated with the user' s social encounters (including so called Familiar Strangers). Motivated by these important observations, we propose a distributed localization scheme called SOMA that is particularly suitable for sparse mobile networks. To exploit the correlation between mobility and social encounters, we formulate the localization process as an optimization problem with the objective of maximizing the probability of visiting a sequence of locations when the user witnesses the given set of social encounters at different time. Employing the Hidden Markov Model, we design an efficient algorithm based on dynamic programming for solving the optimization problem. SOMA is fully distributed, in which each user only makes use of the connectivity information with other users. Since different users may have varying levels of mobility regularity, one critical challenge with SOMA is that a user with weak mobility regularity may result in poor localization accuracy. We introduce the concept of mobility irregularity to distinguish users. Then, one optimization is made to SOMA that allows a user with weak mobility regularity to leverage the locations from the users encounters. Experimental results based on large-scale real traces demonstrate that SOMA achieves much smaller localization error than many state-of-the-art localization schemes, but requires it minimal running time. [ABSTRACT FROM AUTHOR]

Spectrum sensing is essential for the success of the cognitive radio networks. In traditional spectrum sensing schemes, secondary users (SUs) are responsible for the spectrum sensing which could be very time and resource consuming. It leads to a great deal of inefficiency in spectrum usage and introduces many practical challenges. To tackle these challenges and leverage the spectrum opportunity more efficiently, we propose a new system that provides a spectrum sensing service for SUs using dedicated wireless spectrum sensor networks (WSSNs). In this paper we focus on the sensing channel assignment problem in WSSNs and formulate the problem as a sensing effectiveness maximization problem (SEMP). We prove that SEMP is NP-complete under the ideal case, and show that more challenges arise in real environments. To address the issue, we systematically study the design tradeoff and critical factors when maximizing the sensing effectiveness. Based on these study results we propose a sensing channel assignment algorithm (SCAS). We conduct test-bed empirical investigations as well as comprehensive simulations. Performance evaluation results show that for both the scenarios of given deployments and manual deployments, SCAS is able to sense more channels to improve the sensing effectiveness. The improvement is up to 300 % and the average improvement is 150 % compared with other simple alternatives. [ABSTRACT FROM AUTHOR]

Nowadays movement patterns and people's behavioral models are needed for traffic engineers and city planners. These observations could be used to reason about mobility and its sustainability and to support decision makers with reliable information. The very same knowledge about human diaspora and behavior extracted from these data is also valuable to the urban planner, so as to localize new services, organize logistics systems and to detect changes as they occur in the movement behavior. Moreover, it is interesting to investigate movement in places like a shopping area or a working district either for commercial purposes or for improving the service quality. These kinds of tracking data are made available by wireless and mobile communication technologies. It is now possible to record and collect a large amount of mobile phone calls in a city. Technologies for object tracking have recently become affordable and reliable and hence we were able to collect mobile phone data from a city in China from January 1, 2008 to December 31, 2008. The large amount of phone call records from mobile operators can be considered as life mates and sensors of persons to inform howmany people are present in any given area and how many are entering or leaving. Each phone call record usually contains the caller and callee IDs, date and time, and the base station where the phone calls are made. As mobile phones are widely used in our daily life, many human behaviors can be revealed by analyzing mobile phone data. Through mobile phones, we can learn the information about locations, communications between mobile phone users during their daily lives. In this work, we propose a comprehensive visual analysis system named as MViewer, Mobile phone spatiotemporal data Viewer, which is the first system to visualize and analyze the population'smobility patterns from millions of phone call records. Our system consists of three major components: 1) visual analysis of user groups in a base station; 2) visual analysis of the mobility patterns on different user groups making phone calls in certain base stations; 3) visual analysis of handoff phone call records. Some well-established visualization techniques such as parallel coordinates and pixelbased representations have been integrated into our system. We also develop a novel visualization schemes, Voronoidiagram-based visual encoding to reveal the unique features of mobile phone data. We have applied our system to real mobile phone datasets that are kindly provided by our project partners and obtained some interesting findings regarding people's mobility patterns. [ABSTRACT FROM AUTHOR]

In the rising tide of the Internet of things, more and more things in the world are connected to the Internet. Recently, data have kept growing at a rate more than four times of that expected in Moore's law. This explosion of data comes from various sources such as mobile phones, video cameras and sensor networks, which often present multidimensional characteristics. The huge amount of data brings many challenges on the management, transportation, and processing IT infrastructures. To address these challenges, the state-of-art large scale data center networks have begun to provide cloud services that are increasingly prevalent. However, how to build a good data center remains an open challenge. Concurrently, the architecture design, which significantly affects the total performance, is of great research interest. This paper surveys advances in data center network design. In this paper we first introduce the upcoming trends in the data center industry. Then we review some popular design principles for today's data center network architectures. In the third part, we present some up-to-date data center frameworks and make a comprehensive comparison of them. During the comparison, we observe that there is no so-called optimal data center and the design should be different referring to the data placement, replication, processing, and query processing. After that, several existing challenges and limitations are discussed. According to these observations, we point out some possible future research directions. [ABSTRACT FROM AUTHOR]

Duty cycling is a fundamental approach to conserving energy in sensor networks; however, it brings challenges to event detection in the sense that an event may be undetected or undergo a certain delay before it is detected, in particular when sensors are low duty-cycled. We investigate the fundamental relationship between event detection and energy efficiency. We quantify event detection performance by deriving the closed forms of detection delay and detectability with a relatively simple model. We also characterize the intrinsic tradeoff that exists between detection performance and system lifetime, which helps flexible design decisions for sensor networks. In addition, we propose a fully localized algorithm called CAS to cooperatively determine sensor wakeups. Without relying on location information, the distributed algorithm is easy to implement and scalable to network density and scale. Theoretical bounds of event detection are also studied to facilitate comparative study. Comprehensive experiments are conducted and results demonstrate that the proposed algorithm significantly improves event detection performance in terms of detection latency and detection probability. It reduces as high as 31% of detection delay and increases as much as 25% of detectability compared with the random independent scheme. [ABSTRACT FROM AUTHOR]

Information service plays a key role in grid system, handles resource discovery and management process. Employing existing information service architectures suffers from poor scalability, long search response time, and large traffic overhead. In this paper, we propose a service club mechanism, called S-Club, for efficient service discovery. In S-Club, an overlay based on existing Grid Information Service (GIS) mesh network of CROWN is built, so that GISs are organized as service clubs. Each club serves for a certain type of service while each GIS may join one or more clubs. S-Club is adopted in our CROWN Grid and the performance of S-Club is evaluated by comprehensive simulations. The results show that S-Club scheme significantly improves search performance and outperforms existing approaches. [ABSTRACT FROM AUTHOR]

Globally unique ID allocation is usually not applicable in a sensor network due to the massive production of cheap sensor nodes, the limited bandwidth, and the size of the payload. However, locally unique IDs are still necessary for nodes to implement unicast communications to save power consumption. Several solutions have been proposed for locally unique ID assignment in sensor networks. However, they bring much communication overhead, which is not desirable due to the limited power supply in a sensor node. Combined with a directed diffusion communication paradigm, a reactive ID assignment scheme with security mechanisms is proposed in this paper. It defers ID conflict resolution until data communications are initiated and thus saves communication overhead. [ABSTRACT FROM AUTHOR]

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As a joint effort between the Chinese Academy of Sciences and the Hong Kong University of Science and Technology, the BLOSSOMS sensor network project aims to identify research issues at all levels from practical applications down to the design of sensor nodes. In this project, a heterogeneous sensor array including different types of application-dependent sensors as well as monitoring sensors and intruding sensors are being developed. Application-dependent power-aware communication protocols are also being studied for communications among sensor nodes. An ontology-based middleware is built to relieve the burden of application developers from collecting, classifying and processing messy sensing contexts. This project is also developing a set of tools allowing researchers to model, simulate/emulate, analyze, and monitor various functions of sensor networks. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) and Grid computing systems have emerged as popular models aiming at further utilizing Internet information and resources, complementing the available client–server services. However, the mechanism of peers randomly choosing logical neighbors without any knowledge about underlying physical topology can cause a serious topology mismatch problems between the overlay network and the physical underlying network. The topology mismatch problem brings a great stress in the Internet infrastructure and greatly limits the performance gain from various search or routing techniques in P2P and Grid systems. Aiming at alleviating the mismatch problem and reducing the unnecessary traffic, we have proposed two approaches, adaptive overlay topology optimization (AOTO) and location-aware topology matching (LTM) techniques, to reduce the total traffic cost and average query response time. Both AOTO and LTM are scalable and completely distributed in the sense that they do not require any global knowledge of the whole overlay network when each node is optimizing the organization of its logical neighbors. This paper shows the effectiveness of AOTO and LTM and compares the performance of these two approaches through simulation studies. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue, including one by K. Oikonomou and I. Stavrakakis on the problem with medium access control (MAC) and another by O. Simeone and U. Spagnolini on an entirely novel time synchronization approach based on pulse-coupled oscillators.